Application of intelligent nursing system based on big data in maintenance hemodialysis patients

<!DOCTYPE html> <html lang="en" class="pb-page" data-request-id="6ac100b6-fafc-4430-93c8-cc47a7742b27" ><head data-pb-dropzone="head"><meta name="pbContext" content=";requestedJournal:journal:slast;website:website:slast-site;ctype:string:Journal Content;journal:journal:slast;pageGroup:string:Article View;wgroup:string:Migrated Websites;issue:issue:pii\:S2472630324X00059;page:string:Show Full Text;article:article:pii\:S2472630324000426;subPage:string:Full Text;product:product:elsevier\:product\:ha"/>  <meta name="robots" content="noarchive,nocache" /> <meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1, user-scalable=1"/> <meta property="og:title" content="Application of intelligent nursing system based on big data in maintenance hemodialysis patients" /> <meta property="og:type" content="Article" /> <meta property="og:url" content="https://slas-technology.org/article/S2472-6303(24)00042-6/abstract" /> <meta property="og:site_name" content="SLAS Technology" /> <meta property="og:image" content="https://slas-technology.org/cms/asset/5d38049e-2425-4034-959d-891de30c4664/gr1.jpg" /> <meta property="og:description" content="Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD." /> <meta name="twitter:title" content="Application of intelligent nursing system based on big data in maintenance hemodialysis patients" /> <meta name="twitter:image" content="https://slas-technology.org/cms/asset/5d38049e-2425-4034-959d-891de30c4664/gr1.jpg" /> <meta name="twitter:description" content="Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD." /> <meta name="twitter:card" content="summary_large_image"> <link rel="canonical" href="https://slas-technology.org/article/S2472-6303(24)00042-6/fulltext"> <title>Application of intelligent nursing system based on big data in maintenance hemodialysis patients - SLAS Technology</title><meta name="citation_journal_title" content="SLAS Technology"/> <meta name="citation_journal_abbrev" content="SLAS Technology"/> <meta name="citation_publisher" content="Elsevier"/> <meta name="citation_title" content="Application of intelligent nursing system based on big data in maintenance hemodialysis patients"/> <meta name="citation_date" content="2024/08/01"/> <meta name="citation_online_date" content="2024/06/18"/> <meta name="citation_volume" content="29"/> <meta name="citation_issue" content="4"/> <meta name="citation_pmid" content="38901761"/> <meta name="citation_abstract_html_url" content="http://slas-technology.org/article/S2472630324000426/abstract"/> <meta name="citation_fulltext_html_url" content="http://slas-technology.org/article/S2472630324000426/fulltext"/> <meta name="citation_pdf_url" content="http://slas-technology.org/article/S2472630324000426/pdf"/> <meta name="citation_issn" content="2472-6303"/> <meta name="citation_issn" content="2472-6311"/> <meta name="citation_language" content="English"/> <meta name="citation_doi" content="10.1016/j.slast.2024.100160"/> <meta name="citation_pmid" content="38901761"/> <meta name="citation_author" content="Qijun Long"></meta><meta name="citation_author_institution" content="School of Public Health and Administration, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China"></meta><meta name="citation_author_institution" content="College of Innovation and Entrepreneurship, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China"></meta><meta name="citation_author" content="Meichun Yang"></meta><meta name="citation_author_email" content="Ymc18778699339@163.com"></meta><meta name="citation_author_institution" content="Affiliated Hospital, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China"></meta><meta name="citation_author_institution" content="College for International Education, Philippine Christian University, Manila 1004, Philippines"></meta><meta name="citation_author" content="Jiahui Long"></meta><meta name="citation_author_institution" content="Medical College, Xinjiang University of Science & Technology, Korla 841009, Xinjiang, China"></meta><meta name="citation_keywords" content="Maintenance hemodialysis patients; Intelligent nursing system; Nursing satisfaction; Self-care ability; Self-efficacy intervention"></meta><meta name="citation_reference" content="citation_title=Effects of expanded hemodialysis with medium cut-off membranes on maintenance hemodialysis patients: a review; citation_author=Z. 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Bhatt; citation_journal_title=J Org End User Comput; citation_publication_date=2022; "></meta><meta name="citation_reference" content="citation_title=A two-echelon responsive health analytic model for triggering care plan revision in geriatric care management; citation_author=V. Tang; citation_author=H.Y. Lam; citation_author=C.H. Wu; citation_author=G.T.S. Ho; citation_journal_title=J Org End User Comput; citation_publication_date=2022; "></meta><meta name="citation_reference" content="citation_title=Smart home health monitoring system for predicting type 2 diabetes and hypertension; citation_author=S.P. Chatrati; citation_journal_title=J King Saud Univ-Comput Inf Sci; citation_volume=34; citation_number=3; citation_firstpage=862; citation_lastpage=870; citation_publication_date=2022; "></meta><meta name="citation_abstract" content="<h2>Abstract</h2><p>Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD. Through experiments, it has been found that compared to self-efficacy intervention, intelligent nursing systems can control the concurrent rate of MHD patients below 14 %, and self-efficacy intervention methods can control the concurrent rate of MHD patients above 13 %. Moreover, using intelligent nursing systems can improve the ability of MHD patients to self-care. At the same time, before the use of intelligent nursing systems, the nursing satisfaction of MHD patients also varied greatly, with the overall satisfaction rate after use being 70 % higher than before. Using intelligent nursing systems can improve the satisfaction of MHD patients with nursing outcomes.</p>"></meta><meta charset="UTF-8" /> <script type="text/javascript"> ;window.NREUM||(NREUM={});NREUM.init={privacy:{cookies_enabled:true}}; window.NREUM||(NREUM={}),__nr_require=function(t,e,n){function r(n){if(!e[n]){var o=e[n]={exports:{}};t[n][0].call(o.exports,function(e){var o=t[n][1][e];return r(o||e)},o,o.exports)}return e[n].exports}if("function"==typeof __nr_require)return __nr_require;for(var o=0;o<n.length;o++)r(n[o]);return r}({1:[function(t,e,n){function r(t){try{c.console&&console.log(t)}catch(e){}}var o,i=t("ee"),a=t(26),c={};try{o=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(c.console=!0,o.indexOf("dev")!==-1&&(c.dev=!0),o.indexOf("nr_dev")!==-1&&(c.nrDev=!0))}catch(s){}c.nrDev&&i.on("internal-error",function(t){r(t.stack)}),c.dev&&i.on("fn-err",function(t,e,n){r(n.stack)}),c.dev&&(r("NR AGENT IN DEVELOPMENT MODE"),r("flags: 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left: 0 !important; bottom: 0; margin: auto; width: 100vw; height: 95vh; -webkit-transform: translateZ(0) !important; display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: vertical; -webkit-box-direction: normal; -ms-flex-direction: column; flex-direction: column; -webkit-box-align: center; -ms-flex-align: center; align-items: center; -webkit-box-pack: center; -ms-flex-pack: center; justify-content: center; text-align: center; } </style> <script class="optanon-category-4" type="text/plain"> //-------------------------------------------------------------------------- var gpt_Version = '04072023'; var gpt_AdFile = $('#gpt-Variables').data('adfile'); var gpt_MaxMOATInitAttempts = 11; var gpt_MaxMOATReadyAttempts = 11; var gpt_MaxContextualReadyAttempts = 21; var gpt_MOATInitAttempts = 0; var gpt_MOATReadyAttempts = 0; var gpt_ContextualReadyAttempts = 0; var gpt_IsProd = true; var gpt_StartTime; var gs_channels = 'DEFAULT'; var gpt_ContextualReady = false; var gpt_ContextualURL = 'Not enabled outside of Production'; var gpt_DisableAdsFlagFound = false; var gptEvent_Debug = ''; var gptCustomTargeting_Debug = ''; var gptSite_Debug = ""; var gptPage_Debug = ""; var gptContextualEnabled = false; var gptContextualResult_Debug = ''; var gptContextualInit_Debug = ''; var gptContextualURL_Debug = ''; var gptContextualReady_Debug = ''; var gptMoatReady_Debug = ''; var gptMoatInit_Debug = ''; var gptTestENV_Debug = ''; var gptDisableAds_Debug = ''; var gptStatus_Debug = 'GPT Still Loading - Refresh Values'; var gptInvalidAdSize_Debug = ''; //-------------------------------------------------------------------------- $(document).ready(function () { //Check to see if we are on a development env gpt_CheckEnvironment(); if (!gpt_DisableAdsFlagFound) { gpt_StartTime = new Date(); if (gpt_AdFile !== "PU") { //Once the dom has loaded, begin the process to init the ads. gpt_InitScripts(); gpt_CheckForMOATInit(); } } }); </script> <script type="text/javascript"> function gpt_CheckEnvironment() { if ($('#gptDisableAds').length > 0) { gptDisableAds_Debug = 'Flag Detected - Ads Disabled'; gpt_DisableAdsFlagFound = true; } else { gptDisableAds_Debug = 'Ads Enabled'; gpt_DisableAdsFlagFound = false; } switch (gpt_AdFile) { case 'PU': gptContextualEnabled = true; if ($("#Environment").val() !== "Prod") { gpt_IsProd = false; gptTestENV_Debug = 'Test Environment Detected'; } else { gptTestENV_Debug = 'Production Environment Detected'; } break; case 'JBS': case 'LANCET': gptContextualEnabled = true; if ($(".gpt-ad").length > 0) { //Other ads exist, allow the Prestitial $(document.body).append("<div class='gpt-ad' data-adsize='prestitial'></div>"); } if (window.location.hostname.toLowerCase().indexOf('.literatumonline.') >= 0) { gpt_IsProd = false; gptTestENV_Debug = 'Test Environment Detected'; } else { gptTestENV_Debug = 'Production Environment Detected'; } break; default: //Cell gptContextualEnabled = false; if (window.location.hostname.toLowerCase().indexOf('.literatumonline.') >= 0) { gpt_IsProd = false; gptTestENV_Debug = 'Test Environment Detected'; } else { gptTestENV_Debug = 'Production Environment Detected'; } break; } } //-------------------------------------------------------------------------- function gpt_GetNonCMEArticle() { var url; $('.articleCitation').each(function (i, obj) { if ($(obj).find('.toc__item__detials').length > 0) { var itemDetails = $(obj).first().find('.toc__item__detials'); var cmeFound = 0; if (cmeFound == 0) { cmeFound = $(itemDetails).find("img[title~='CME']").length; } if (cmeFound == 0) { cmeFound = $(itemDetails).find("img[title~='(CME)']").length; } if (cmeFound == 0) { cmeFound = $(itemDetails).find("img[title~='Education']").length; } if (cmeFound == 0) { cmeFound = $(itemDetails).find("img[title~='education']").length; } if (cmeFound == 0) { var itemLink = $(itemDetails).first().find('.toc__item__title'); var linkCount = $(itemLink).first().find("a:contains('CME')").length; if (linkCount == 0) { url = $(itemLink).first().find('a').attr('href'); return false; //Found a URL, break the loop } } } }); return url; } //-------------------------------------------------------------------------- function gpt_GetElapsedTime() { var endTime = new Date(); var timeDiff = endTime - gpt_StartTime; //in ms // strip the ms timeDiff /= 1000; // get seconds return seconds = Math.round(timeDiff) + " seconds"; } //-------------------------------------------------------------------------- function gpt_CheckSSC() { //Check for ScreenShot Client var gpt_SSC = gpt_GetQuerystring('gpt_ssc', ''); if (gpt_SSC == '1') { //it's the ScreenShotClient, disable MOAT IVT Detection $('#gpt-Variables').append('<input type="hidden" id="gptm_data">'); $('#gptm_data').val(0); gpt_Target("m_data"); } } //-------------------------------------------------------------------------- function gpt_LocalSS(adTypeId) { window.scrollTo(0, 0); gpt_Sleep(1000); gpt_ScrollToAd(adTypeId); gpt_PreTearsheet(adTypeId); } //-------------------------------------------------------------------------- function gpt_InitScripts() { if (gpt_AdFile == "PU") { if (gpt_IsProd) { gpt_ContextualURL = gpt_CheckForNULL($('#canonicalurl').val()); if (gpt_ContextualURL === null) { gpt_ContextualURL = window.location.href.split('?')[0].split('#')[0]; } else { gpt_ContextualURL = window.location.origin + gpt_ContextualURL; } var useSSL = 'https:' == document.location.protocol; var gs = document.createElement('script'); gs.async = true; gs.type = 'text/javascript'; gs.src = (useSSL ? 'https:' : 'http:') + '//elsevier.gscontxt.net/multizone/channels.cgi?url=' + encodeURIComponent(gpt_ContextualURL); gs.onload = function () { gpt_ContextualReady = true; }; document.head.appendChild(gs); } } else { //JBS-Cell-Lancet var useSSL = 'https:' == document.location.protocol; var gads = document.createElement('script'); gads.async = true; gads.type = 'text/javascript'; gads.src = (useSSL ? 'https:' : 'http:') + '//securepubads.g.doubleclick.net/tag/js/gpt.js'; document.head.appendChild(gads); if (gpt_IsProd && gptContextualEnabled) { gpt_ContextualURL = window.location.href.split('?')[0].split('#')[0]; var gs = document.createElement('script'); gs.async = true; gs.type = 'text/javascript'; gs.src = (useSSL ? 'https:' : 'http:') + '//elsevier.gscontxt.net/multizone/channels.cgi?url=' + encodeURIComponent(gpt_ContextualURL); gs.onload = function () { gpt_ContextualReady = true; }; document.head.appendChild(gs); } var elsevierMoatHeader = document.createElement('link'); elsevierMoatHeader.rel = 'preload'; elsevierMoatHeader.as = 'script'; elsevierMoatHeader.src = (useSSL ? 'https:' : 'http:') + '//z.moatads.com/elsevierheader150204004183/moatheader.js'; document.head.appendChild(elsevierMoatHeader); var elsevierMoatAds = document.createElement('link'); elsevierMoatAds.rel = 'preconnect'; elsevierMoatAds.src = (useSSL ? 'https:' : 'http:') + '//mb.moatads.com'; document.head.appendChild(elsevierMoatAds); var moatHeader = document.createElement('script'); moatHeader.async = true; gads.type = 'text/javascript'; moatHeader.src = (useSSL ? 'https:' : 'http:') + '//z.moatads.com/elsevierheader150204004183/moatheader.js'; document.head.appendChild(moatHeader); } } //-------------------------------------------------------------------------- async function gpt_CheckForMOATInit() { //Check every 1/10 of a second until MOAT is Loaded or 1 second timeout if (gpt_MOATInitAttempts < gpt_MaxMOATInitAttempts) { gpt_MOATInitAttempts += 1; if (typeof window.moatPrebidApi === "object") { //MOAT LOADED gptMoatInit_Debug = '0.0' + gpt_MOATInitAttempts + ' seconds'; gpt_CheckForMOATReady(); } else { setTimeout(() => { gpt_CheckForMOATInit(); }, 100); } } else { //TIMEOUT: MOAT Not LOADED gptMoatInit_Debug = 'Timeout Expired'; var googletag = googletag || {}; googletag.cmd = googletag.cmd || []; gpt_CheckForContextualReady(); } } //-------------------------------------------------------------------------- function gpt_CheckForMOATReady() { //Check every 1/10 of a second until MOAT is Ready or 1 second timeout if (gpt_MOATReadyAttempts < gpt_MaxMOATReadyAttempts) { gpt_MOATReadyAttempts += 1; if (window.moatPrebidApi.safetyDataAvailable()) { //MOAT Ready gptMoatReady_Debug = '0.0' + gpt_MOATReadyAttempts + " seconds"; gpt_CheckForContextualReady(); } else { setTimeout(() => { gpt_CheckForMOATReady(); }, 100); } } else { //TIMEOUT: MOAT Not Ready gptMoatReady_Debug = 'Timeout Expired'; gpt_CheckForContextualReady(); } } //-------------------------------------------------------------------------- function gpt_CheckForContextualReady() { //Check every 1/10 of a second until Contextual is Ready or 2 second timeout if (gpt_IsProd && gptContextualEnabled) { //Enabled if (gpt_ContextualReadyAttempts < gpt_MaxContextualReadyAttempts) { gpt_ContextualReadyAttempts += 1; if (gpt_ContextualReady) { //Contextual Ready gptContextualURL_Debug = gpt_ContextualURL; gptContextualReady_Debug = '0.0' + gpt_ContextualReadyAttempts + " seconds"; gptContextualInit_Debug = gs_channels; gpt_InitAds(); } else { setTimeout(() => { gpt_CheckForContextualReady(); }, 100); } } else { //LOG TIMEOUT error //REMOVE FOR NOW (SHOULD LOG TO A DIFF LOCATION) //TIMEOUT: Contextual Not Ready $('#gpt_GrapeShotDebugBtn').remove(); gptContextualURL_Debug = gpt_ContextualURL; gptContextualReady_Debug = 'Timeout Expired'; gptContextualInit_Debug = ""; gpt_InitAds(); } } else { //Not Enabled $('#gpt_GrapeShotDebugBtn').remove(); switch (gpt_AdFile) { case 'PU': case 'LANCET': case 'JBS': gptContextualURL_Debug = 'Not enabled outside of Production'; gptContextualReady_Debug = 'Not enabled outside of Production'; gptContextualInit_Debug = 'Not enabled outside of Production'; break; case "CELLJOURNAL": case "CELLBUCKET": gptContextualReady_Debug = 'Not enabled on Cell'; gptContextualInit_Debug = 'Not enabled on Cell'; gptContextualURL_Debug = 'Not enabled on Cell'; break; } gpt_InitAds(); } } //-------------------------------------------------------------------------- function GPT_GetPageByURL() { var host = window.location.host.toLocaleLowerCase() var localpath = window.location.pathname.toLocaleLowerCase(); ///Check for Cell.com if (host.indexOf('cell.com') >= 0) { return null; } ///Check for Cell.com (marlin-stag) if (host.indexOf('www-cell-com') >= 0) { return null; } ///Check for Lancet if (host.indexOf('thelancet') >= 0) { return null; } if (localpath.indexOf('/article/') >= 0) { return 'article.fulltext'; } if (localpath.indexOf('/action/dosearch') >= 0) { return 'search.searchResults'; } if (localpath.indexOf('/content/') >= 0) { return 'periodical.editorial'; } return 'periodical.home'; } //-------------------------------------------------------------------------- function gpt_CheckForNULL(x, valueifnull) { if (typeof valueifnull == "undefined") { valueifnull = null; } if (typeof x == 'undefined') { return valueifnull; } if (x == null) { return valueifnull; } if (x == "null") { return valueifnull; } if ($.trim(x) == '') { return valueifnull; } return x; } //-------------------------------------------------------------------------- function gpt_GetAdditionalTargeting() { //adds page level targeting of custom variables //the id (minus gpt) and the value will be used as a key-value pair //eg: <input type="hidden" id="gptissuepii" value="x" class="gpt-Target"> //eg: target issuepii=x $(".gpt-Target").each(function (index) { var gptTargetId = $(this).attr('id'); gptTargetId = gptTargetId.replace('gpt', '').toLowerCase(); if (gptTargetId !== '') { gpt_Target(gptTargetId); } }) } //-------------------------------------------------------------------------- function gpt_GetQuerystring(key, default_) { //function to read querystring variables if (default_ === null) default_ = ''; key = key.replace(/[\[]/, "\\\[").replace(/[\]]/, "\\\]"); var regex = new RegExp("[\\?&]" + key + "=([^&#]*)"); var qs = regex.exec(window.location.href); if (qs === null) return default_; else return qs[1]; } //-------------------------------------------------------------------------- function gpt_GetDFPTestId() { //Get any request for a test id from querystring and set custom targeting parameter var gptDFPTestId = gpt_GetQuerystring('dfptestid', ''); if (gptDFPTestId !== '') { $('#gpt-Variables').append('<input type="hidden" id="gptDFPTestId">'); $('#gptDFPTestId').val(gptDFPTestId); gpt_Target("DFPTestId"); } } //-------------------------------------------------------------------------- function gpt_Target(targetName) { //Set page level target if value is found if ($('#gpt' + targetName).length > 0) { var targetValue = $('#gpt' + targetName).val(); if (targetValue !== '') { targetValue = targetValue.replace(/[^-a-z0-9]/ig, ''); //Only Allow a-z, 0-9 googletag.pubads().setTargeting(targetName.toLowerCase(), targetValue.toLowerCase()); gptCustomTargeting_Debug = gptCustomTargeting_Debug + targetName.toLowerCase() + ' = ' + targetValue.toLowerCase() + '<br>'; } } } //-------------------------------------------------------------------------- function gpt_TargetAccessType() { var currentAccessType = $('#gptAccessType').val(); if (typeof currentAccessType == "undefined") { currentAccessType = ""; } if (currentAccessType.indexOf('ANON_IP') >= 0) { googletag.pubads().setTargeting("loggedin", "yes"); } else { gpt_Target("LoggedIn"); } } //-------------------------------------------------------------------------- async function gpt_PreTearsheet(adTypeId) { if (typeof adTypeId === "undefined") { adTypeId = 0; } //Remove Elsevier Cookie Policy Notice if ($("#onetrust-consent-sdk").length > 0) { $("#onetrust-consent-sdk").remove(); } //Mobile Nav $('#skip').remove(); $('#skipNavigationLink').remove(); //JBS Modals if ($('.leadinModal').length > 0) { $('.leadinModal').remove(); } if ($('.leadinModal').length > 0) { $('.leadinModal').remove(); } if ($('.usabilla__overlay').length > 0) { $('.usabilla__overlay').remove(); } //Pendo if ($('._pendo-step-container').length > 0) { $('._pendo-step-container').remove(); } if ($('._pendo-guide-tt_').length > 0) { $('._pendo-guide-tt_').remove(); } if ($('._pendo-image').length > 0) { $('._pendo-image').remove(); } if ($('._pendo-badge-image').length > 0) { $('._pendo-badge-image').remove(); } switch (gpt_AdFile) { case "CELLJOURNAL": case "CELLBUCKET": //Mobile Bug Fix on Cell if (parseInt(adTypeId) == 5) { $('.widget-horizontalAd').css('position', 'static'); $('.body-horizontalAd').css('display', 'block'); } break; case "JBS": case "LANCET": objLeaderboard = ".container-leaderboard"; offset = 53; switch (parseInt(adTypeId)) { case 3: //MPU case 4: //SkyScraper //BUGFIX: Show Sidebar ad for JBS //skyscraper & mpu if ($('.widget-verticalAd').length > 0) { $('.widget-verticalAd').css('display', 'block'); } $('#backgroundLayer').css('display', 'none'); //Hide any Prestitial break; case 6: //Prestitial $('#backgroundLayer').css('display', 'block'); break; default: $('#backgroundLayer').css('display', 'none'); //Hide any Prestitial break; } break; } } //-------------------------------------------------------------------------- function gpt_Sleep(ms) { return new Promise(resolve => setTimeout(resolve, ms)); } //-------------------------------------------------------------------------- async function gpt_ScrollToAd(adTypeId) { if (typeof adTypeId === "undefined") { adTypeId = 0; } var adId = ""; var offset = 0; var objLeaderboard = ""; var gpt_ScrollToAd = true; switch (gpt_AdFile) { case "PU": //PracticeUpdate var hideOtherAds = true; $('#modalPrestitial').modal('hide'); $('.jumbotron-welcome').hide(); objLeaderboard = ".container-leaderboard"; offset = 53; switch (parseInt(adTypeId)) { case 2: //LeaderBoard, no Scroll Needed gpt_ScrollToAd = false; adId = "adSlot1"; break; case 3: //MPU adId = "adSlot2"; break; case 4: //SkyScraper adId = "adSlot3"; $('.most-read').hide(); break; case 5: //Mobile, no Scroll Needed gpt_ScrollToAd = false; adId = "adSlot1"; break; case 6: //Interstitial gpt_ScrollToAd = false; adId = "adSlot15"; await gpt_Sleep(1000); $(objLeaderboard).hide(); $('#modalPrestitial').unbind(); $('#modalPrestitial').modal(); break; case 7: //Native Ad break; case 10: //Master Companion hideOtherAds = false; gpt_ScrollToAd = false; break; default: //Unknown Ad Type gpt_ScrollToAd = false; adId = "adSlot1"; break; } if (hideOtherAds) { //Hide all other ads $('.ad-container').each(function (i, obj) { if (this.id !== adId) { var tempParent = this.id; tempParent = tempParent.replace("adSlot", "adParent"); $("#" + tempParent).addClass('hide'); } }); } break; case "JBS": objLeaderboard = ".js__adToHide"; offset = 80; switch (parseInt(adTypeId)) { case 2: //Leaderboard gpt_ScrollToAd = false; $(".widget-verticalAd").hide(); break; case 5: //Mobile //Scroll to Top gpt_ScrollToAd = false; break; case 3: //MPU case 4: //SkyScraper $(objLeaderboard).hide(); adId = $('.gpt-ad-defined[data-adsize="sidebar"').attr('id'); break; case 10: //MasterCompanion gpt_ScrollToAd = false; break; case 13: //Phone - Portrait case 14: //Phone - Landscape $(objLeaderboard).hide(); offset = 60; adId = 'gpt-Interscroller'; gpt_ScrollToAd = true; break; default: //Unknown Ad Type gpt_ScrollToAd = false; break; } break; case "CELLJOURNAL": case "CELLBUCKET": objLeaderboard = ".js__adToHide"; offset = 260; switch (parseInt(adTypeId)) { case 2: //Leaderboard gpt_ScrollToAd = false; $('.body-verticalAd').hide(); break; case 5: //Mobile //Scroll to Top gpt_ScrollToAd = false; break; case 3: //MPU case 4: //SkyScraper $(objLeaderboard).hide(); adId = $('.gpt-ad-defined[data-adsize="boombox"').attr('id'); break; case 10: //MasterCompanion gpt_ScrollToAd = false; break; default: //Unknown Ad Type gpt_ScrollToAd = false; break; } //Hide the second ad if found var tCount = 0; $(".widget.literatumAd.none.GPT-ad-placeholder.widget-horizontalAd.widget-compact-all").each(function (index) { tCount += 1; if (tCount !== 1) { $(this).hide(); } }); break; case "LANCET": objLeaderboard = ".js__adToHide"; offset = 80; switch (parseInt(adTypeId)) { case 2: //Leaderboard gpt_ScrollToAd = false; $('.body-verticalAd').hide(); break; case 5: //Mobile //Scroll to Top gpt_ScrollToAd = false; break; case 3: //MPU case 4: //SkyScraper $(objLeaderboard).hide(); adId = $('.gpt-ad-defined[data-adsize="skyscraper"').attr('id'); break; case 10: //MasterCompanion gpt_ScrollToAd = false; break; default: //Unknown Ad Type gpt_ScrollToAd = false; break; } break; } //Scroll to Top var gpt_container = $("html,body"); gpt_container.animate({ scrollTop: 0, scrollLeft: 0 }); if (gpt_ScrollToAd) { //If Adexists, scroll to it var gpt_scrollTo = $('#' + adId); if (gpt_scrollTo.length > 0) { //Scroll to the Ad var gpt_topPosition = gpt_scrollTo.offset().top - gpt_container.offset().top + gpt_container.scrollTop(); if (gpt_topPosition > 0) { gpt_container.animate({ scrollTop: gpt_topPosition, scrollLeft: 0 }); if (gpt_topPosition - offset > 0 && offset > 0) { gpt_container.animate({ scrollTop: gpt_topPosition - offset, scrollLeft: 0 }); } } } } } //-------------------------------------------------------------------------- async function gpt_CheckForCookiePolicy() { var returnVal = false; if ($('#onetrust-accept-btn-handler').length > 0) { $('#onetrust-accept-btn-handler').click(); returnVal = true; } return returnVal; } //-------------------------------------------------------------------------- function gpt_InitAds() { var gpt_ResponsiveHomepage = false; switch (gpt_AdFile) { case "JBS": case "CELLJOURNAL": case "CELLBUCKET": //Responsive Check if ($('#hdrHomeBrand').length > 0) { gpt_ResponsiveHomepage = true; } break; } //InterScroller Check if (gpt_AdFile === "JBS") { var gpt_InterScrollerEnabled = false; var gpt_InterScrollerInsertObj; if ($("#articleHeader").length > 0) { //It's an article Page if ($('.article__sections > section').length > 0) { gpt_InterScrollerEnabled = true; gpt_InterScrollerInsertObj = $(".article__sections > section"); } } else { if ($('.toc__section').length > 0) { //It's a TOC Page gpt_InterScrollerEnabled = true; gpt_InterScrollerInsertObj = $(".toc__section").first(); } } //Add the Interscroller if (gpt_InterScrollerEnabled) { gpt_InterScrollerInsertObj.first().append($("#gpt-Interscroller").clone()); $("#gpt-Interscroller").remove(); $(".gpt-adinterscroller").addClass("gpt-ad"); } } googletag.cmd.push(function () { var gptBoomBoxSizeMapping; var gptFluidMapping; var gptHomePageSidebarMapping; var gptCellHomePageSidebarMapping; var gptInterscrollerMapping; var gptLeaderboardSizeMapping; var gptMobileSizeMapping; var gptPrestitialMapping; var gptRectangleShortSizeMapping; var gptRectangleSizeMapping; var gptSideBarSizeMapping; var gptTallSizeMapping; var gptWideSizeMapping; var gptWideRectangleSizeMapping; //*********************************************** //Start Define Size mapping based on client Viewport //*********************************************** if (gpt_AdFile === 'PU') { gptTallSizeMapping = googletag.sizeMapping(). addSize([992, 0], [[300, 600], [160, 600], [120, 600]]). addSize([0, 0], []). build(); gptRectangleSizeMapping = googletag.sizeMapping(). addSize([992, 0], [[300, 250]]). addSize([0, 0], []). build(); gptRectangleShortSizeMapping = googletag.sizeMapping(). addSize([992, 0], [[300, 250], [320, 50], [300, 50]]). addSize([0, 0], [[300, 250], [320, 50], [300, 50]]). build(); gptWideSizeMapping = googletag.sizeMapping(). addSize([992, 0], [[728, 90]]). addSize([760, 0], [[728, 90], [320, 50], [300, 50]]). addSize([0, 0], [[320, 50], [300, 50]]). build(); gptWideRectangleSizeMapping = googletag.sizeMapping(). addSize([992, 0], []). addSize([760, 0], [[320, 50], [300, 50], [300, 250]]). addSize([0, 0], [[320, 50], [300, 50], [300, 250]]). build(); gptMobileSizeMapping = googletag.sizeMapping(). addSize([0, 0], [[320, 50], [300, 50]]). build(); gptFluidMapping = googletag.sizeMapping(). addSize([0, 0], ['fluid']). build(); gptPrestitialMapping = googletag.sizeMapping(). addSize([760, 0], [[1, 1]]). build(); } else { //JBS-CELL-LANCET gptLeaderboardSizeMapping = googletag.sizeMapping(). addSize([992, 0], [[728, 90]]). // Desktop addSize([768, 0], [[728, 90]]). // Tablet Landscape addSize([0, 0], [[320, 50], [300, 50]]). // Phone & Tablet Portrait build(); gptHomePageSidebarMapping = googletag.sizeMapping(). addSize([2090, 795], [[300, 600], [300, 250], [160, 600], [120, 600]]). addSize([1910, 795], [[160, 600], [120, 600]]). addSize([0, 0], []). build(); gptCellHomePageSidebarMapping = googletag.sizeMapping(). addSize([2090, 795], [[300, 600], [300, 250], [336, 280], [160, 600], [120, 600]]). addSize([1910, 795], [[160, 600], [120, 600]]). addSize([0, 0], []). build(); gptSideBarSizeMapping = googletag.sizeMapping(). addSize([1140, 795], [[300, 600], [160, 600], [120, 600], [300, 250]]). // SkyScraper - Desktop (Require min 630 vertical) addSize([1140, 445], [[300, 250]]). // BoomBox Fall Back - Desktop (Require min 280 vertical) addSize([0, 0], []). // Phone build(); gptBoomBoxSizeMapping = googletag.sizeMapping(). addSize([768, 0], [[300, 250], [336, 280]]). // Desktop & Tablet Landscape addSize([0, 0], []). // Phone build(); gptFluidMapping = googletag.sizeMapping(). addSize([0, 0], ['fluid']). build(); gptPrestitialMapping = googletag.sizeMapping(). addSize([600, 700], [[1, 1]]). // Desktop & Tablet Landscape addSize([0, 0], []). // Phone build(); gptInterscrollerMapping = googletag.sizeMapping(). addSize([1140, 445], []). addSize([1024, 768], [[1024, 768]]). addSize([768, 1024], [[768, 1024]]). addSize([480, 320], [[480, 320]]). addSize([320, 480], [[320, 480]]). build(); } //*********************************************** //End Define Size mapping based on client Viewport //*********************************************** //*********************************************** //Start detection for ad slots on page //*********************************************** var gptMasterPage = gpt_CheckForNULL($('#gptPage').val()); var gptMasterSite; if (gpt_AdFile === 'PU') { gptMasterSite = $('#gptSite').val(); } else { //JBS-CELL-LANCET gptMasterSite = "dev"; if (gpt_IsProd) { gptMasterSite = gpt_CheckForNULL($('#gptSite').val()); //If there is no master site, check for a defaultsite if (gptMasterSite == null) { gptMasterSite = gpt_CheckForNULL($('#gptDefaultSite').val()); } } else { //Dev ENV //If the page has no gptSite Value, assume no value which will disable ads if (gpt_CheckForNULL($('#gptSite').val()) === null) { gptMasterSite = null; } } //If there is no master Page, check for a Page using the URL if (gptMasterPage === null) { gptMasterPage = GPT_GetPageByURL(); } } $(".gpt-ad").each(function (index) { var gptPage = ''; var adSizeMapping = ''; var gptPOS; var gptTC; var gptNative; var adId; var gptSite; var tempSlot = null; if (gpt_IsProd) { //Check if the ad slot has it's own site defined if (typeof $(this).data('site') === 'undefined') { gptSite = gptMasterSite; } else { gptSite = gpt_CheckForNULL($(this).data('site')); } } else { gptSite = gptMasterSite; } if (gpt_AdFile === 'PU') { adId = $(this).attr('id'); } else { //JBS-CELL-LANCET adId = 'gpt-ad-' + index + 1; } if (gpt_CheckForNULL(gptSite) !== null) { gptSite_Debug = gptSite; gptPOS = parseInt($(this).data('pos')) || -1; if (gptPOS === -1) { gptPOS = index + 1; } //Check if the ad slot has it's own page defined gptPage = $(this).data('page'); if (gpt_CheckForNULL(gptPage) == null) { //Use the Master Page gptPage = gptMasterPage; } //If the page is not null, preceed it with a slash if (gptPage !== null) { gptPage = '/' + gptPage; } else { gptPage = ''; } gptPage_Debug = gptPage; gptTC = $(this).data('tc'); if (typeof gptTC === "undefined") { gptTC = ''; } gptNative = $(this).data('native'); if (typeof gptNative === "undefined") { gptNative = ''; } //Determine sizemapping based on data-adsize if (gpt_AdFile === 'PU') { switch ($(this).data('adsize')) { case 'tall': adSizeMapping = gptTallSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[120, 600], [160, 600], [300, 600]], adId); break; case 'rectangle': adSizeMapping = gptRectangleSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[300, 250]], adId); break; case 'rectangleshort': adSizeMapping = gptRectangleShortSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[300, 250], [320, 50], [300, 50]], adId); break; case 'wide': adSizeMapping = gptWideSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[728, 90], [320, 50], [300, 50]], adId); break; case 'widerectangle': adSizeMapping = gptWideRectangleSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[320, 50], [300, 50], [300, 250]], adId); break; case 'mobile': adSizeMapping = gptMobileSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[320, 50], [300, 50]], adId); break; case 'fluid': adSizeMapping = gptFluidMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, 'fluid', adId); break; case 'prestitial': adSizeMapping = gptPrestitialMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[1, 1]], adId); break; } } else { //JBS-CELL-LANCET switch ($(this).data('adsize')) { case 'cell-leaderboard': case 'leaderboard': adSizeMapping = gptLeaderboardSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[728, 90], [320, 50], [300, 50]], adId); break; case 'bottom': if (gpt_ResponsiveHomepage) { adSizeMapping = gptHomePageSidebarMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[120, 600], [160, 600], [300, 600], [300, 250]], adId); } else { adSizeMapping = gptBoomBoxSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[300, 250]], adId); } break; case 'boombox': if (gpt_AdFile === "CELLJOURNAL" || gpt_AdFile == "CELLBUCKET") { if (gpt_ResponsiveHomepage) { adSizeMapping = gptCellHomePageSidebarMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[120, 600], [160, 600], [300, 600], [300, 250], [336, 280]], adId); } else { adSizeMapping = gptBoomBoxSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[300, 250], [336, 280]], adId); } } else { adSizeMapping = gptBoomBoxSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[300, 250]], adId); } break; case 'interscroller': adSizeMapping = gptInterscrollerMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[320, 480], [480, 320], [768, 1024], [1024, 768]], adId); break; case 'sidebar': case 'skyscraper': adSizeMapping = gptSideBarSizeMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, [[120, 600], [160, 600], [300, 600], [300, 250]], adId); break; case 'native': case 'fluid': adSizeMapping = gptFluidMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, 'fluid', adId); break; case 'prestitial': adSizeMapping = gptPrestitialMapping; tempSlot = googletag.defineSlot('/6053/els.' + gptSite + gptPage, 'fluid', adId); break; } } } //Ignore any invalid adsizes that could be on the page if (adSizeMapping !== '') { $(this).addClass('gpt-ad-defined'); if (gpt_AdFile !== 'PU') { $(this).attr('id', adId); } tempSlot.defineSizeMapping(adSizeMapping).addService(googletag.pubads()).setTargeting("pos", gptPOS); if (gptTC !== '') { tempSlot.setTargeting("tc", gptTC); } if (gptNative !== '') { tempSlot.setTargeting("native", gptNative); } gptEvent_Debug = gptEvent_Debug + $(this).data('adsize') + ' Requested<br>'; } else { gptInvalidAdSize_Debug = gptInvalidAdSize_Debug + "Invalid Size Found, Id: " + $(this).prop('id') + ', adSize: ' + $(this).data('adsize') + '<br>'; $(this).remove(); } }); if (gpt_CheckForNULL(gptInvalidAdSize_Debug) === null) { gptInvalidAdSize_Debug = "None Detected"; } //*********************************************** //End detection for ad slots on page //*********************************************** //*********************************************** //Start page level targeting //*********************************************** if (gpt_AdFile !== 'PU') { //Logged in or IP Access gpt_TargetAccessType(); //Check for Screenshot Client gpt_CheckSSC(); } //Check for DFP Test gpt_GetDFPTestId(); //Check for Additional Targeting gpt_GetAdditionalTargeting(); if (gpt_IsProd && gptContextualEnabled) { var gpt_ContextualRetry = false; //Check for Contextual Targeting if (gs_channels !== "DEFAULT") { var gpt_ContextualCategories = Array.from(gs_channels); for (var i = 0; i < gpt_ContextualCategories.length; i++) { if (gpt_ContextualCategories[i] == 'gx_retry') { gpt_ContextualRetry = true; } if (!gpt_ContextualCategories[i].startsWith("custom_")) { //Remove any tag that is not custom gpt_ContextualCategories.splice(i, 1); i--; } else { //Strip the 'custom_' from the tag gpt_ContextualCategories[i] = gpt_ContextualCategories[i].replace("custom_", "") } } if (gpt_ContextualCategories.length > 0) { googletag.pubads().setTargeting("gs_cat", gpt_ContextualCategories); } else { gpt_ContextualCategories = 'none'; } if (gpt_ContextualRetry) { gpt_ContextualCategories = "*Indexing*"; } gptContextualResult_Debug = gpt_ContextualCategories; } else { gptContextualResult_Debug = 'No segments found'; } } else { //Not Prod gptContextualResult_Debug = 'Not enabled'; var gpt_DevSite = gpt_CheckForNULL($('#gptSite').val(), "dev"); $('#gpt-Variables').append('<input type="hidden" id="gptEnvironment" value="' + gpt_DevSite + '">'); gpt_Target("Environment"); } //*********************************************** //End page level targeting //*********************************************** //Enable google dfp services if (parseInt($('#gptLazyLoading').val()) === 1) { googletag.pubads().enableLazyLoad({ fetchMarginPercent: 200, renderMarginPercent: 0, mobileScaling: 2.0 }); } else { googletag.pubads().enableSingleRequest(); } //Add Listener for RenderEnded googletag.pubads().addEventListener('slotRenderEnded', function (event) { if (!event.isEmpty) { if (typeof LocalAdLoaded === "function") { LocalAdLoaded(event); } if (gpt_ResponsiveHomepage) { if (typeof positionAd === "function") { positionAd(); } } if (event.size[0] == "480" || event.size[1] == "480") { $('.gpt-InScrollBox').css('display', 'block'); gptEvent_Debug = gptEvent_Debug + 'Interscroller ' + event.size + ' rendered: ' + gpt_GetElapsedTime() + '<br>'; } else { gptEvent_Debug = gptEvent_Debug + event.size + ' rendered: ' + gpt_GetElapsedTime() + '<br>'; } } }); if (gpt_AdFile !== 'PU') { googletag.pubads().collapseEmptyDivs(); } googletag.enableServices(); //Populate Defined Ad Slots $(".gpt-ad-defined").each(function (index) { var adId = $(this).attr('id'); googletag.cmd.push(function () { googletag.display(adId); }); }); gptStatus_Debug = 'GPT Complete'; }); } //-------------------------------------------------------------------------- function gpt_DisplayDebug() { //Clone the template and append to Body $(document.body).append($('#gpt-DebugDiv').clone()); $('#gpt-DebugDiv').remove(); //Populate the Values gpt_LoadDebugValues(); //Display and scroll to debug section $('.gpt-DebugDiv').css('display', 'block'); window.scrollTo(0, document.body.scrollHeight); } //-------------------------------------------------------------------------- function gpt_LoadDebugValues() { $('.gpt-DebugBtn').blur(); $('.gptVersion_Debug').text(gpt_Version); $('.gptAdFile_Debug').html(gpt_AdFile); $('.gptEvent_Debug').html(gptEvent_Debug); $('.gptCustomTargeting_Debug').html(gptCustomTargeting_Debug); $('.gptSite_Debug').text(gptSite_Debug); $('.gptContextualResult_Debug').text(gptContextualResult_Debug); $('.gptContextualInit_Debug').text(gptContextualInit_Debug); $('.gptContextualURL_Debug').text(gptContextualURL_Debug); 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typeof="ScholarlyArticle" data-pii="S2472-6303(24)00042-6" lang="en" dir="ltr"><div data-core-wrapper="header"><header data-extent="frontmatter"><div class="core-container"><div data-article-access="full" data-article-access-type="open" class="meta-panel"><span class="meta-panel__left"><span class="meta-panel__type"><span>Full Length Article</span></span></span><span class="meta-panel__middle"><span class="meta-panel__volumeIssue"><a href="/issue/S2472-6303(24)X0005-9"><span>Volume 29</span>, <span>Issue 4</span></a></span><span class="meta-panel__number"><span>100160</span></span><span class="meta-panel__onlineDate">August 2024</span></span><span class="meta-panel__right"><span class="meta-panel__access meta-panel__access--open"><span>Open access</span></span></span></div><h1 property="name">Application of intelligent nursing system based on big data in maintenance hemodialysis patients</h1><div class="contributors"><span class="authors"><span role="list"><span property="author" typeof="Person" role="listitem"><a href="#au0001"><span property="givenName">Qijun</span> <span property="familyName">Long</span></a><sup class="xref"><a href="#aff0001" property="affiliation" typeof="Organization" role="doc-noteref">a</a>,<a href="#aff0002" property="affiliation" typeof="Organization" role="doc-noteref">b</a></sup></span> ∙ <span property="author" typeof="Person" role="listitem" class="corresponding-author"><a href="#au0002"><span property="givenName">Meichun</span> <span property="familyName">Yang</span></a><sup class="xref"><a href="#aff0003" property="affiliation" typeof="Organization" role="doc-noteref">c</a>,<a href="#aff0004" property="affiliation" typeof="Organization" role="doc-noteref">d</a></sup> <a id="ead0001" href="mailto:Ymc18778699339@163.com" property="email" title="Send email to Meichun Yang">Ymc18778699339@163.com</a></span> ∙ <span property="author" typeof="Person" role="listitem"><a href="#au0003"><span property="givenName">Jiahui</span> <span property="familyName">Long</span></a><sup class="xref"><a href="#aff0005" property="affiliation" typeof="Organization" role="doc-noteref">e</a></sup></span></span></span></div><div class="core-self-citation"><div id="core-affiliations-notes"><div class="affiliations"><div property="affiliation" typeof="Organization"><span class="label">a</span><span id="aff0001" property="name">School of Public Health and Administration, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div><div property="affiliation" typeof="Organization"><span class="label">b</span><span id="aff0002" property="name">College of Innovation and Entrepreneurship, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div><div property="affiliation" typeof="Organization"><span class="label">c</span><span id="aff0003" property="name">Affiliated Hospital, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div><div property="affiliation" typeof="Organization"><span class="label">d</span><span id="aff0004" property="name">College for International Education, Philippine Christian University, Manila 1004, Philippines</span></div><div property="affiliation" typeof="Organization"><span class="label">e</span><span id="aff0005" property="name">Medical College, Xinjiang University of Science & Technology, Korla 841009, Xinjiang, China</span></div></div></div><div id="core-content-info"><div class="history"><div>Publication History:</div><div><span>Received March 14, 2024</span>; <span>Revised May 22, 2024</span>; <span>Accepted June 18, 2024</span>; <span>Published online June 18, 2024</span></div></div><div class="core-links"><span class="doi">DOI: <a href="https://doi.org/10.1016/j.slast.2024.100160" property="sameAs" target="_blank">10.1016/j.slast.2024.100160</a></span><span class="science-direct">Also available on <a href="https://www.sciencedirect.com/science/article/pii/S2472630324000426" target="_blank">ScienceDirect</a></span></div><div role="paragraph">Copyright: © 2024 The Authors. 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role="paragraph">Maintenance hemodialysis (MHD) is one of the most important renal replacement therapies for patients with end-stage renal disease. However, long-term and frequent treatment not only damages the physiological functions of patients but also leads to serious economic burdens and mental stress. This can easily cause a series of psychological disorders in patients, resulting in severe rejection and fear of MHD. To reduce patient resistance and improve the quality of life of MHD, this article built an intelligent nursing system based on big data and then used the constructed intelligent nursing system to research MHD. Through experiments, it has been found that compared to self-efficacy intervention, intelligent nursing systems can control the concurrent rate of MHD patients below 14 %, and self-efficacy intervention methods can control the concurrent rate of MHD patients above 13 %. Moreover, using intelligent nursing systems can improve the ability of MHD patients to self-care. At the same time, before the use of intelligent nursing systems, the nursing satisfaction of MHD patients also varied greatly, with the overall satisfaction rate after use being 70 % higher than before. Using intelligent nursing systems can improve the satisfaction of MHD patients with nursing outcomes.</div></section></div></div><div class="core-linked-content"></div><section id="keywords" property="keywords"><h2>Keywords</h2><ol><li><a href="/action/doSearch?AllField="Maintenance+hemodialysis+patients"&journalCode=slast" alt="Search on this keyword">Maintenance hemodialysis patients</a></li><li><a href="/action/doSearch?AllField="Intelligent+nursing+system"&journalCode=slast" alt="Search on this keyword">Intelligent nursing system</a></li><li><a href="/action/doSearch?AllField="Nursing+satisfaction"&journalCode=slast" alt="Search on this keyword">Nursing satisfaction</a></li><li><a href="/action/doSearch?AllField="Self-care+ability"&journalCode=slast" alt="Search on this keyword">Self-care ability</a></li><li><a href="/action/doSearch?AllField="Self-efficacy+intervention"&journalCode=slast" alt="Search on this keyword">Self-efficacy intervention</a></li></ol></section><div class="core-linked-content"></div><div class="core-linked-content"></div><div class="core-linked-content"></div><section id="bodymatter" data-extent="bodymatter" property="articleBody" typeof="Text"><div class="core-container"><section id="sec-1"><h2>1 Introduction</h2><div id="para0007" role="paragraph">An intelligent nursing system based on big data can solve the problems of elderly care and nursing brought about by the aging population to a certain extent and can enable users to live high-quality nursing lives in their daily lives without the constraints of time and environment. In recent years, people's quality of life has been increasing, and their lifestyles have become increasingly rich. However, unreasonable living habits often induce the occurrence of some chronic diseases, which in turn lead to an increase in kidney system diseases year by year. End-stage renal disease (ESRD) is an irreversible chronic progressive disease, which is one of the major diseases threatening human health. The patient has no obvious discomfort symptoms at the initial stage. When it reaches the stage of renal failure, various symptoms of uremia would appear, requiring treatment such as HD and kidney transplantation to maintain life. Hemodialysis (HD) is an important renal replacement therapy commonly used in ESRD, which can achieve the goal of prolonging a patient's life through several steps. Through the establishment of the venous indwelling tube or autologous arteriovenous fistula, the principle of dispersion and convection of dialyzer is used to remove metabolic waste and excess water from the body, maintain electrolyte and acid-base balance, and thus continue life. Long-term HD can cause a significant psychological and physiological burden on MHD patients, and affect the quality of life of elderly patients. Therefore, this article used an intelligent nursing system to care for MHD patients, which is conducive to strengthening the intervention of nursing personnel and improving the confidence of patients in treating diseases, thereby providing better nursing services for patients and reducing their anxiety and depression.</div><div id="para0008" role="paragraph">MHD can prolong the survival period of ESRD patients, but it cannot completely cure the disease. Long-term repeated arteriovenous puncture, diet control, and huge treatment costs not only seriously damage the physiological functions of patients, but also have a huge psychological impact. Therefore, many scholars have researched how to better care for and treat MHD patients [<a id="crf0007" href="#bib0001" role="doc-biblioref" data-xml-rid="bib0001">1</a>,<a id="crf0008" href="#bib0002" role="doc-biblioref" data-xml-rid="bib0002">2</a>]. Macdougall Iain C believes that intravenous iron supplementation is the standard treatment for HD patients, but comparative data on clinically effective treatment options are limited. To this end, he randomly assigned adults undergoing MHD to receive high doses of sucrose iron. The experiment found that among patients undergoing HD, the active high-dose intravenous iron supplementation regimen was superior to the passive low-dose regimen [<a id="crf0009" href="#bib0003" role="doc-biblioref" data-xml-rid="bib0003">3</a>]. Mehrotra Rajnish believed that although depression is common among patients receiving MHD, the data on their treatment is limited. By comparing cognitive behavioral therapy with sertraline therapy, he found that patients receiving MHD have no impact on their treatment for depression [<a id="crf0010" href="#bib0004" role="doc-biblioref" data-xml-rid="bib0004">4</a>]. Gomes Neto Mansueto explored the impact of different intradialytic exercise training methods on physical function and health-related quality of life in patients with MHD. He found that aerobic exercise itself was not significantly correlated with the improvement of aerobic capacity and quality of life, but combining intradialytic aerobic exercise and resistance exercise can improve the physical function and quality of life in patients with end-stage renal disease on HD [<a id="crf0011" href="#bib0005" role="doc-biblioref" data-xml-rid="bib0005">5</a>]. Drew David A believed that patients undergoing MHD generally have cognitive impairment, and identifying good screening tests for cognitive impairment can provide a broader assessment of dialysis facilities, thereby optimizing education and medical management [<a id="crf0012" href="#bib0006" role="doc-biblioref" data-xml-rid="bib0006">6</a>]. Many researchers have researched MHD using various research methods, but few experts and scholars consider using intelligent nursing systems to study it.</div><div id="para0009" role="paragraph">Some scholars have different research perspectives on MHD patients. Zhang Qing-Lai believed that the outbreak of novel coronavirus pneumonia has caused considerable psychological pressure on HD patients, leading to unhealthy psychological moods. Therefore, in the process of prevention and control of novel coronavirus, it is of great significance to carry out strict management and meticulous nursing intervention on MHD patients [<a id="crf0013" href="#bib0007" role="doc-biblioref" data-xml-rid="bib0007">7</a>]. Yuan Huaihong investigated the correlation between frailty status and psychosocial factors in MHD patients and believed that among MHD patients, risk factors for frailty include age, comorbidity, depression, and sleep disorders. Through experiments, it was found that patients' age, sleep quality, and depression are positively correlated [<a id="crf0014" href="#bib0008" role="doc-biblioref" data-xml-rid="bib0008">8</a>]. Silver Samuel A found that there is very little data to reveal the impact of routine blood testing frequency on the prognosis and nursing costs of patients undergoing MHD. Therefore, he changed the blood testing frequency from 4 weeks to 6 weeks and found that nursing costs have decreased [<a id="crf0015" href="#bib0009" role="doc-biblioref" data-xml-rid="bib0009">9</a>]. Jiang Xinxin believed that the hospital mortality rate of ESRD patients is 6–8 times higher than that of the general population, largely due to the weakness of ESRD patients [<a id="crf0016" href="#bib0010" role="doc-biblioref" data-xml-rid="bib0010">10</a>]. Experts and scholars have researched MHD, so there is a theoretical basis for selecting them for research in this article.</div><div id="para0010" role="paragraph">Currently, for patients with advanced renal disease, MHD is one of the most important means of clinical treatment. However, due to the very long treatment process, the cycle of HD for patients is also very long, which would bring a huge psychological burden to patients and their families, and also increase the economic burden of the family. This can lead to negative emotions such as anxiety and depression in patients, which can have a very negative effect on the treatment effect and seriously affect the prognosis and quality of life of patients. To this end, this article used big data to establish an intelligent nursing system and used it to research MHD. It was found that using intelligent nursing systems can improve the ability of MHD patients to self-care and can help MHD patients build complications during dialysis, thereby improving their satisfaction with nursing outcomes.</div><div id="para0011" role="paragraph">The research framework of this paper is shown in Figure <a id="crf0017" href="#fig0001">Fig. 1</a>.</div><div class="figure-wrap"><figure id="fig0001" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/5d38049e-2425-4034-959d-891de30c4664/main.assets/gr1.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 1:" src="/cms/10.1016/j.slast.2024.100160/asset/5d38049e-2425-4034-959d-891de30c4664/main.assets/gr1.jpg" height="662" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0001-title" class="figure__title"><span class="label figure__label">Fig. 1</span> <span class="figure__title__text">Research framework.</span></div></figcaption></figure></div></section><section id="sec-2"><h2>2 Exploration methods related to intelligent nursing systems and MHD patients</h2><section id="sec-2-1"><h3>2.1 MHD</h3><div id="para0012" role="paragraph">MHD is one of the most common renal replacement therapies for patients with end-stage renal disease. Its principle is to utilize the diffusion and convection functions of hemodialyzers to expel something from the body, thereby achieving the goal of correcting electrolyte disorders and acid-base balance and purifying the blood [<a id="crf0018" href="#bib0011" role="doc-biblioref" data-xml-rid="bib0011">11</a>,<a id="crf0019" href="#bib0012" role="doc-biblioref" data-xml-rid="bib0012">12</a>]. Renal failure patients have never experienced a significant state of uremia through HD. After initial HD, the symptoms of uremia gradually shift to a stable dialysis stage. The process of regularly implementing regular dialysis treatment is known as MHD. MHD technology has made tremendous progress so far, with many benefits, for example, it can save the lives of patients with kidney failure and maintain the lives of patients with kidney failure, help patients discharge metabolic waste from the body, can quickly pull out water from the body, relieve the symptoms of heart failure, excrete toxins and water from the patient's body, and maintain the acid-base and electrolyte balance in the patient's body, but it has not yet been able to completely replace the function of normal kidneys [<a id="crf0020" href="#bib0013" role="doc-biblioref" data-xml-rid="bib0013">13</a>]. The combined effects of disease and dialysis treatment have brought a series of pathological and physiological changes to the patient's body. Without effective management and prevention of dialysis in patients with end-stage renal disease, it can lead to further accelerated development of patient weakness, which can seriously threaten the quality of life and prognosis of patients. The huge MHD population, the weak high incidence rate, and the adverse consequences caused by the weakness would bring a very heavy burden to families and society, which requires the high attention of medical staff [<a id="crf0021" href="#bib0014" role="doc-biblioref" data-xml-rid="bib0014">14</a>]. The implementation process of MHD is shown in <a id="crf0022" href="#fig0002">Fig. 2</a>.</div><div class="figure-wrap"><figure id="fig0002" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/fad5197b-1259-40a0-a387-e6fd3aad9a19/main.assets/gr2.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 2:" src="/cms/10.1016/j.slast.2024.100160/asset/fad5197b-1259-40a0-a387-e6fd3aad9a19/main.assets/gr2.jpg" height="379" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0002-title" class="figure__title"><span class="label figure__label">Fig. 2</span> <span class="figure__title__text">Implementation process of MHD.</span></div></figcaption></figure></div></section><section id="sec-2-2"><h3>2.2 Self efficacy theory</h3><div id="para0013" role="paragraph">Self-efficacy generally refers to the relationship between people and the belief that one can successfully achieve and complete a behavioral goal or handle a difficult situation [<a id="crf0023" href="#bib0015" role="doc-biblioref" data-xml-rid="bib0015">15</a>,<a id="crf0024" href="#bib0016" role="doc-biblioref" data-xml-rid="bib0016">16</a>]. Self-efficacy is usually mainly influenced by four different sources of information, which can produce varying degrees of efficacy. It mainly includes the influence of four sources of information: direct experience, verbal persuasion, indirect experience, and emotional arousal. Direct experience refers mainly to the experience of individuals acquiring relevant abilities through personal experience. Verbal persuasion refers to persuasive encouragement, advice, or some other language that can enhance people's beliefs and the likelihood of success. Indirect experience refers to the role of role models. Research has found that when people are tired and depressed, they often make negative judgments about events in their lives, which can lead to a very low sense of self-efficacy. Emotional arousal: Physical conditions and emotional feelings can affect people's self-assessment ability. Studies have found that when people are tired and depressed, they often make negative judgments about life events, and their self-efficacy will be lower. Self-efficacy theory has achieved many positive results in promoting health, especially in chronic disease care, and can predict the self-management level and prognosis of chronic disease patients to a certain extent. The self-efficacy theory is also very good for intervention in HD patients. Using it can not only improve HD patients' awareness of their health, but also further standardize their health behaviors, and enhance their confidence in treating diseases and the quality of life of ESRD patients [<a id="crf0025" href="#bib0017" role="doc-biblioref" data-xml-rid="bib0017">17</a>,<a id="crf0026" href="#bib0018" role="doc-biblioref" data-xml-rid="bib0018">18</a>]. The theoretical framework of self-efficacy is shown in <a id="crf0027" href="#fig0003">Fig. 3</a>.</div><div class="figure-wrap"><figure id="fig0003" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/8bc9dfb8-988c-46f4-a29f-9ce10e1ab407/main.assets/gr3.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 3:" src="/cms/10.1016/j.slast.2024.100160/asset/8bc9dfb8-988c-46f4-a29f-9ce10e1ab407/main.assets/gr3.jpg" height="361" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0003-title" class="figure__title"><span class="label figure__label">Fig. 3</span> <span class="figure__title__text">Theoretical framework of self-efficacy.</span></div></figcaption></figure></div></section><section id="sec-2-3"><h3>2.3 Intelligent nursing system</h3><div id="para0014" role="paragraph">An intelligent nursing system is a concrete manifestation of applying big data to nursing mode. In other words, the elderly and chronic patients can enjoy a variety of high-quality nursing services in their own homes or other places without the constraints of time and environment in daily life, thereby meeting the personalized requirements of users and their pursuit of quality of life [<a id="crf0028" href="#bib0019" role="doc-biblioref" data-xml-rid="bib0019">19</a>,<a id="crf0029" href="#bib0020" role="doc-biblioref" data-xml-rid="bib0020">20</a>]. Intelligent nursing systems are based on big data technology and collect user information through various sensors. Based on this information, they monitor user activities, allowing children or caregivers to remotely view the daily lives of users, especially their health status. In the event of some accidents or dangerous events, such as changes in physical conditions, sudden diseases, etc., it can automatically alarm or notify children or caregivers. In addition, intelligent nursing systems collect physical indicator data for the elderly through intelligent devices, and then automatically diagnose and prevent diseases for the elderly through expert medical systems. This is very important in all aspects. The design module diagram of the intelligent nursing system is shown in <a id="crf0030" href="#fig0004">Fig. 4</a>.</div><div class="figure-wrap"><figure id="fig0004" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/bf60f24e-13ae-4576-962c-aa51791a8c6a/main.assets/gr4.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 4:" src="/cms/10.1016/j.slast.2024.100160/asset/bf60f24e-13ae-4576-962c-aa51791a8c6a/main.assets/gr4.jpg" height="504" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0004-title" class="figure__title"><span class="label figure__label">Fig. 4</span> <span class="figure__title__text">Design module diagram of intelligent nursing system.</span></div></figcaption></figure></div><div id="para0015" role="paragraph">In the process of designing and implementing an intelligent nursing system, it is necessary to meet some practical needs by studying the dialysis data information of patients through a series of steps on the data platform. Realize the collection, processing, storage, analysis, and management of patient data and information through the data platform [<a id="crf0031" href="#bib0021" role="doc-biblioref" data-xml-rid="bib0021">21</a>]. Best practice recommendations are made through the assessment, diagnosis, and diagnosis of the patient's care, and are matched with the nursing goal assessment. By summarizing various patient data, a vertical dataset for deep data mining is formed.</div></section><section id="sec-2-4"><h3>2.4 Application of intelligent nursing system to MHD patients</h3><section id="sec-2-4-1"><h4>2.4.1 Establishment of the system</h4><div id="para0016" role="paragraph">To better study the application of intelligent nursing systems in MHD patients, big data computing can be used to formally model intelligent nursing systems and further study MHD. This requires an appropriate description of dialysis and nursing systems. There is too much to explain in the specifications described, and unnecessary restrictions can be imposed to eliminate previously unreasonable implementations. The system environment is the environment of the entire intelligent nursing system, which interacts with the environment where the system is located and obtains information from the environment. Modeling a system environment is the modeling of environmental inputs to a system, which includes two aspects. Analyze the system environment and system design of the intelligent nursing system separately. System design modeling refers to modeling the architecture of an intelligent home care system. The intelligent nursing system framework is shown in <a id="crf0032" href="#fig0005">Fig. 5</a>.</div><div class="figure-wrap"><figure id="fig0005" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/2a26bb08-dc56-4a6c-989f-f6ea8ba8238b/main.assets/gr5.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 5:" src="/cms/10.1016/j.slast.2024.100160/asset/2a26bb08-dc56-4a6c-989f-f6ea8ba8238b/main.assets/gr5.jpg" height="401" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0005-title" class="figure__title"><span class="label figure__label">Fig. 5</span> <span class="figure__title__text">Intelligent nursing system framework.</span></div></figcaption></figure></div><div id="para0017" role="paragraph">The oxygen content in arterial blood plays a crucial role in the health of the human body. The definition formula of blood oxygen is shown in Formula <a id="crf0033" href="#eqn0001">(1)</a>:<div id="eqn0001" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><msub><mi>S</mi><mi>p</mi></msub><msub><mi>O</mi><mn>2</mn></msub><mo linebreak="goodbreak">=</mo><mrow><mo stretchy="true">(</mo><mfrac><msub><mi>T</mi><mrow><mi>H</mi><mi>b</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></msub><mrow><msub><mi>T</mi><mrow><mi>H</mi><mi>b</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></msub><mo>+</mo><msub><mi>T</mi><mrow><mi>H</mi><mi>b</mi></mrow></msub></mrow></mfrac><mo stretchy="true">)</mo></mrow><mo linebreak="goodbreak">*</mo><mn>100</mn><mo>%</mo></mrow></math></div></div><div class="label">(1)</div></div></div><div id="para0018" role="paragraph">Among them, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><msub><mi>T</mi><mrow><mi>H</mi><mi>b</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></msub></math> is the solubility of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>H</mi><mi>b</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></math>; <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><msub><mi>T</mi><mrow><mi>H</mi><mi>b</mi></mrow></msub></math> is the solubility of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>H</mi><mi>b</mi></mrow></math>; <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><msub><mi>S</mi><mi>p</mi></msub><msub><mi>O</mi><mn>2</mn></msub></mrow></math> is the blood oxygen value.</div><div id="para0019" role="paragraph">With the regular beating of the heart, the blood flow in the human arteries would exhibit waveform changes. The calculation formula is shown in Formula <a id="crf0034" href="#eqn0002">(2)</a>:<div id="eqn0002" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>i</mi><msub><mi>n</mi><mi>i</mi></msub><mrow><mo stretchy="true">[</mo><mrow><mrow><mo>(</mo><msub><mi>F</mi><mrow><mi>A</mi><mi>B</mi></mrow></msub><mo linebreak="badbreak">+</mo><msub><mi>F</mi><mrow><mi>B</mi><mi>C</mi></mrow></msub><mo>)</mo></mrow><mo linebreak="badbreak">/</mo><msub><mi>F</mi><mrow><mi>B</mi><mi>C</mi></mrow></msub></mrow><mo stretchy="true">]</mo></mrow><mo linebreak="goodbreak">=</mo><mo>−</mo><mrow><mo stretchy="true">(</mo><mrow><msub><mi>X</mi><mn>1</mn></msub><msub><mi>Y</mi><mn>1</mn></msub><mo linebreak="badbreak">+</mo><msub><mi>X</mi><mn>2</mn></msub><msub><mi>Y</mi><mn>2</mn></msub></mrow><mo stretchy="true">)</mo></mrow><mstyle mathvariant="normal"><mi>Δ</mi></mstyle><mi>H</mi><mo linebreak="goodbreak">+</mo><msub><mi>F</mi><mrow><mi>B</mi><mi>C</mi></mrow></msub><mo linebreak="goodbreak">*</mo><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mrow><mo stretchy="true">(</mo><mrow><msub><mi>X</mi><mn>1</mn></msub><msub><mi>Y</mi><mn>1</mn></msub><mo>+</mo><msub><mi>X</mi><mn>2</mn></msub><msub><mi>Y</mi><mn>2</mn></msub></mrow><mo stretchy="true">)</mo></mrow><mstyle mathvariant="normal"><mi>Δ</mi></mstyle><mi>H</mi></mrow></msup></mrow></math></div></div><div class="label">(2)</div></div></div><div id="para0020" role="paragraph">The system uses sensors to collect raw signals from patients' pulse waves, which can be affected by the surrounding environment and the patient's psychological state. Therefore, before information extraction, the information signal must be pre-processed, and the calculation is shown in Formula <a id="crf0035" href="#eqn0003">(3)</a>:<div id="eqn0003" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><msub><mi>Y</mi><mi>k</mi></msub><mo linebreak="goodbreak">=</mo><mfrac><mrow><msub><mi>y</mi><mi>k</mi></msub><mo>+</mo><msub><mi>y</mi><mrow><mi>k</mi><mo>+</mo><mn>1</mn></mrow></msub><mo>+</mo><msub><mi>y</mi><mrow><mi>k</mi><mo>+</mo><mn>2</mn></mrow></msub><mo>+</mo><msub><mi>y</mi><mrow><mi>k</mi><mo>+</mo><mn>3</mn></mrow></msub><mo>+</mo><mo>⋯</mo><mo>+</mo><msub><mi>y</mi><mrow><mi>k</mi><mo>+</mo><mi>m</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msub><mi>y</mi><mrow><mi>k</mi><mo>+</mo><mi>m</mi></mrow></msub></mrow><mi>m</mi></mfrac></mrow></math></div></div><div class="label">(3)</div></div></div><div id="para0021" role="paragraph">The heart rate of the human body is consistent with the pulse rate, so the heart rate can be estimated by calculating the pulse rate. The calculation formula is shown in Formula <a id="crf0036" href="#eqn0004">(4)</a>:<div id="eqn0004" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>p</mi><mi>e</mi><mi>a</mi><msub><mi>k</mi><mo>−</mo></msub><mi>i</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>v</mi><mi>a</mi><msub><mi>l</mi><mo>−</mo></msub><mi>n</mi><mi>u</mi><mi>m</mi><mo linebreak="goodbreak">=</mo><mfrac><mrow><msubsup><mo>∑</mo><mi>j</mi><mrow><mi>m</mi><mo>−</mo><mn>1</mn></mrow></msubsup><mrow><mo stretchy="true">(</mo><mrow><mi>p</mi><mi>e</mi><mi>a</mi><msub><mi>k</mi><mrow><mi>j</mi><mo>+</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>p</mi><mi>e</mi><mi>a</mi><msub><mi>k</mi><mi>j</mi></msub></mrow><mo stretchy="true">)</mo></mrow></mrow><mrow><mi>m</mi><mo>−</mo><mn>1</mn></mrow></mfrac><mo linebreak="goodbreak">+</mo><mfrac><mrow><mi>x</mi><mi>y</mi></mrow><mrow><mi>p</mi><mi>e</mi><mi>a</mi><msub><mi>k</mi><mo>−</mo></msub><mi>i</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>v</mi><mi>a</mi><msub><mi>l</mi><mo>−</mo></msub><mi>n</mi><mi>u</mi><mi>m</mi></mrow></mfrac></mrow></math></div></div><div class="label">(4)</div></div></div><div id="para0022" role="paragraph">Among them, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>p</mi><mi>e</mi><mi>a</mi><mi>k</mi><mspace width="0.33em"></mspace><mi>i</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>v</mi><mi>a</mi><msub><mi>l</mi><mo>−</mo></msub><mi>n</mi><mi>u</mi><mi>m</mi></mrow></math> represents the average value between two adjacent peaks within a period of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mi>m</mi></math>, and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>x</mi><mi>y</mi></mrow></math> represents the sampling frequency.</div><div id="para0023" role="paragraph">The maximum and minimum values of pulse wave signals collected by the intelligent nursing system represent direct current and alternating current components and are used to detect negative emotions such as depression and anxiety in HD patients. The calculation formula is shown in Formula <a id="crf0037" href="#eqn0005">(5)</a>:<div id="eqn0005" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><msup><mrow><mi>H</mi></mrow><mi>t</mi></msup><mo linebreak="goodbreak">=</mo><mfrac><mfrac><mrow><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mrow><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow><mo>/</mo><mn>2</mn></mrow></mfrac><mfrac><mrow><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mrow><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow><mo>/</mo><mn>2</mn></mrow></mfrac></mfrac><mo linebreak="goodbreak">=</mo><mfrac><mrow><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>+</mo><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow><mo>*</mo><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow></mrow><mrow><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>+</mo><msub><mi>L</mi><mrow><mi>r</mi><mi>e</mi><mi>d</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow><mo>*</mo><mrow><mo stretchy="true">(</mo><mrow><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>−</mo><msub><mi>L</mi><mrow><mi>i</mi><mi>r</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub></mrow><mo stretchy="true">)</mo></mrow></mrow></mfrac></mrow></math></div></div><div class="label">(5)</div></div></div></section><section id="sec-2-4-2"><h4>2.4.2 Nursing of MHD patients</h4><div id="para0024" role="paragraph">(1) Establishing a good nurse-patient relationship</div><div id="para0025" role="paragraph">By developing intelligent nursing plans for some nursing personnel in the HD department, these nursing personnel can easily implement a dark system nursing plan, which improves the enthusiasm of nursing personnel. It is necessary to help nursing personnel truly achieve patient focus, follow service concepts, and clarify the working principles of HD, thereby helping patients plan their daily nutrition. Nursing personnel can also comprehensively improve themselves from multiple aspects by using intelligent nursing entities. We must pay attention to our quality and nursing expertise, serve patients with a good state of spiritual civilization and solid nursing skills, and help patients build confidence in defeating diseases, only by thinking about problems from the perspective of patients and learning to establish a friendly relationship of equality and mutual trust with patients can we open the hearts of patients. Allowing medical personnel to timely grasp the psychological changes of patients.</div><div id="para0026" role="paragraph">(2) Dynamic observation of blood pressure changes</div><div id="para0027" role="paragraph">Using the intelligent nursing system, nursing personnel can help patients monitor their blood pressure before, during, and after HD, which can more intuitively observe the specific changes in patient's blood pressure, thereby helping patients maintain a stable blood pressure environment. In addition, nursing personnel should also teach patients and their families the correct methods and precautions for using intelligent nursing systems to measure blood pressure.</div><div id="para0028" role="paragraph">(3) Interventions in music therapy</div><div id="para0029" role="paragraph">Music relaxation therapy helps relieve the nerves of patients with tension and anxiety, helping patients alleviate physical pain, and improving the quality of sleep for patients as much as possible [<a id="crf0038" href="#bib0022" role="doc-biblioref" data-xml-rid="bib0022">22</a>]. Music therapy can actively alleviate the anxiety of HD patients during their first treatment, thereby enabling them to smoothly transition to HD treatment. The main reason for this is that when patients master music therapy, they can find music suitable for themselves through intelligent nursing systems whenever their body is anxious. Through music therapy and conscious regulation of the sympathetic nerves, it is possible to reduce the secretion of adrenaline, thereby alleviating physical tension.</div><div id="para0030" role="paragraph">(4) Psychological counseling</div><div id="para0031" role="paragraph">Patients with uremia generally have certain psychological disorders. When nurses use the intelligent nursing system to care for and treat patients every day, they must listen to the patient's words, understand the patient's specific psychological activities, and solve some problems that the patient may have promptly. At the same time, the use of the intelligent nursing system can explain to the patient on time the purpose of treatment, the necessity of hemodialysis, cooperation in the treatment process, and matters that need attention. It can help to better explain the analysis process of hemodialysis, prepare patients mentally, cooperate with treatment, and return to the ward to rest in time after hemodialysis. The nurse warmly received the patients.</div><div id="para0032" role="paragraph">Considering that the sequence of user-related actions in the system may be uncertain when modeling a single process, appropriate actions should be selected and combined into abstract events to standardize the sequence of all possible events. The calculation formula is shown in Formula <a id="crf0039" href="#eqn0006">(6)</a>:<div id="eqn0006" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><mi>U</mi><mi>s</mi><mi>e</mi><msub><mi>r</mi><mo>−</mo></msub><mi>H</mi><mi>e</mi><mi>m</mi><mi>o</mi><mi>d</mi><mi>i</mi><mi>a</mi><mi>i</mi><mi>y</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>R</mi><mi>o</mi><mi>o</mi><mi>m</mi><mrow><mo stretchy="true">(</mo><mi>i</mi><mo stretchy="true">)</mo></mrow><mo linebreak="goodbreak">=</mo><mi>t</mi><mi>u</mi><mi>r</mi><mi>n</mi><mi>O</mi><mi>n</mi><mi>T</mi><mi>a</mi><mi>p</mi><mo linebreak="goodbreak">+</mo><mi>H</mi><mi>e</mi><mi>m</mi><mi>o</mi><mi>d</mi><mi>i</mi><mi>a</mi><mi>i</mi><mi>y</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>R</mi><mi>o</mi><mi>o</mi><mi>m</mi></mrow></math></div></div><div class="label">(6)</div></div></div><div id="para0033" role="paragraph">Patients undergoing HD should provide health guidance and relevant dialysis knowledge. Nurses can use intelligent nursing systems to guide patients in monitoring and recording their daily urine, weight, blood pressure, and diet, allowing patients to learn to establish a healthy lifestyle. The use of intelligent nursing systems can provide better guidance on vascular access care, as well as daily dialysis pipeline inspection and partial cleaning. This allows patients to reasonably mix food while protecting the catheter, thereby strictly controlling water intake, and limiting sodium, potassium, and phosphorus intake based on their conditions. With the continuous increase of dialysis treatment for patients, metabolic damage to renal function can more easily lead to loss of systemic nutrients, leading to decreased immune function, and gradually increasing complications, such as some acute complications during dialysis, which can increase the risk of death for patients. The flow chart of the MHD patient blood system based on an intelligent nursing system is shown in <a id="crf0040" href="#fig0006">Fig. 6</a>.</div><div class="figure-wrap"><figure id="fig0006" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/bc98d0d2-02c2-42b6-98a8-a721d9a0075a/main.assets/gr6.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 6:" src="/cms/10.1016/j.slast.2024.100160/asset/bc98d0d2-02c2-42b6-98a8-a721d9a0075a/main.assets/gr6.jpg" height="552" width="791" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0006-title" class="figure__title"><span class="label figure__label">Fig. 6</span> <span class="figure__title__text">Flow chart of MHD patient blood system based on intelligent nursing system.</span></div></figcaption></figure></div><div id="para0034" role="paragraph">The transformation of medical models has led the medical community to increasingly recognize that disease is not only a physical symptom that requires treatment but also a negative life or psychological event that requires coping and processing. The calculation formula is shown in Formula <a id="crf0041" href="#eqn0007">(7)</a>:<div id="eqn0007" class="display-formula"><div class="equation" role="math"><div class="inner"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="No alternative text available"><mrow><msub><mi>H</mi><mi>t</mi></msub><mrow><mo stretchy="true">(</mo><mi>x</mi><mo stretchy="true">)</mo></mrow><mo linebreak="goodbreak">=</mo><msub><mi>a</mi><mn>0</mn></msub><mo linebreak="goodbreak">*</mo><msup><mrow><mo>(</mo><msub><mi>b</mi><mn>0</mn></msub><mo>−</mo><mi>b</mi><mo>)</mo></mrow><mn>2</mn></msup><mo linebreak="goodbreak">+</mo><msub><mi>a</mi><mn>1</mn></msub><mo linebreak="goodbreak">*</mo><msup><mrow><mo>(</mo><msub><mi>b</mi><mn>1</mn></msub><mo>−</mo><mi>b</mi><mo>)</mo></mrow><mn>2</mn></msup><mo linebreak="goodbreak">+</mo><msub><mi>a</mi><mn>2</mn></msub><mo linebreak="goodbreak">*</mo><msup><mrow><mo>(</mo><msub><mi>b</mi><mn>2</mn></msub><mo>−</mo><mi>b</mi><mo>)</mo></mrow><mn>2</mn></msup><mo linebreak="goodbreak">+</mo><mo>⋯</mo><mo>+</mo><msub><mi>a</mi><mi>n</mi></msub><mo linebreak="goodbreak">*</mo><msup><mrow><mo>(</mo><msub><mi>b</mi><mi>n</mi></msub><mo>−</mo><mi>b</mi><mo>)</mo></mrow><mn>2</mn></msup></mrow></math></div></div><div class="label">(7)</div></div></div></section></section></section><section id="sec-3"><h2>3 Object and method</h2><section id="sec-3-1"><h3>3.1 Experimental objects</h3><div id="para0035" role="paragraph">A random sampling method was used to select HD patients from the community and hospital dialysis centers in Area A as the study subjects. Among them, a total of 210 patients met the nanofiltration criteria, and these patients were investigated. 210 questionnaires were distributed and 200 were retrieved. There were 10 unqualified entries, and a total of 200 patients were enrolled in the study. The effective recovery rate of the sample was 95.24 %. The sample size is sufficient for further data analysis. The basic investigation situation of these 200 patients is shown in <a id="crf0042" href="#tbl0001">Table 1</a>.</div><div class="figure-wrap"><figure id="tbl0001" class="table" data-figureviewer-ignore="true"><div class="table-wrap"><table data-figureviewer-ignore="true"><thead><tr class="rowsep" data-xml-align="left" data-xml-valign="top"><th id="en0001">Project</th><th id="en0002">Classification</th><th id="en0003">Number of people</th><th id="en0004">Percentage/%</th></tr></thead><tbody><tr><td id="en0005" rowspan="2">Gender</td><td id="en0006">Male</td><td id="en0007">102</td><td id="en0008">51</td></tr><tr><td id="en0010">Female</td><td id="en0011">98</td><td id="en0012">49</td></tr><tr><td id="en0013" rowspan="4">Age</td><td id="en0014">Under 18 years old</td><td id="en0015">5</td><td id="en0016">2.5</td></tr><tr><td id="en0018">19–40 years old</td><td id="en0019">55</td><td id="en0020">27.5</td></tr><tr><td id="en0022">41–62 years old</td><td id="en0023">80</td><td id="en0024">40</td></tr><tr><td id="en0026">Over 63 years old</td><td id="en0027">60</td><td id="en0028">30</td></tr><tr><td id="en0029" rowspan="3">Marital status</td><td id="en0030">Unmarried</td><td id="en0031">40</td><td id="en0032">20</td></tr><tr><td id="en0034">Married</td><td id="en0035">100</td><td id="en0036">50</td></tr><tr><td id="en0038">Divorced/widowed</td><td id="en0039">60</td><td id="en0040">30</td></tr><tr><td id="en0041" rowspan="3">Payment method for medical expenses</td><td id="en0042">Rural cooperative medical care</td><td id="en0043">80</td><td id="en0044">40</td></tr><tr><td id="en0046">Social security</td><td id="en0047">90</td><td id="en0048">45</td></tr><tr><td id="en0050">At your own expense</td><td id="en0051">30</td><td id="en0052">15</td></tr><tr><td id="en0053" rowspan="4">Primary pathogenesis</td><td id="en0054">Chronic nephritis</td><td id="en0055">50</td><td id="en0056">25</td></tr><tr><td id="en0058">Diabetes</td><td id="en0059">60</td><td id="en0060">30</td></tr><tr><td id="en0062">High blood pressure</td><td id="en0063">45</td><td id="en0064">22.5</td></tr><tr><td id="en0066">Other diseases</td><td id="en0067">45</td><td id="en0068">22.5</td></tr><tr><td id="en0069" rowspan="3">Dialysis years</td><td id="en0070">Less than 5 years</td><td id="en0071">65</td><td id="en0072">32.5</td></tr><tr><td id="en0074">6–10 years</td><td id="en0075">80</td><td id="en0076">40</td></tr><tr><td id="en0078">More than 11 years</td><td id="en0079">55</td><td id="en0080">27.5</td></tr><tr><td id="en0081" rowspan="3">Monthly income</td><td id="en0082">Below 1500 yuan</td><td id="en0083">30</td><td id="en0084">15</td></tr><tr><td id="en0086">1501–3000 yuan</td><td id="en0087">110</td><td id="en0088">55</td></tr><tr><td id="en0090">Above 3001 yuan</td><td id="en0091">60</td><td id="en0092">30</td></tr><tr><td id="en0093" rowspan="4">Complications during dialysis</td><td id="en0094">None</td><td id="en0095">10</td><td id="en0096">5</td></tr><tr><td id="en0098">1 kind</td><td id="en0099">50</td><td id="en0100">25</td></tr><tr><td id="en0102">2 kinds</td><td id="en0103">100</td><td id="en0104">50</td></tr><tr><td id="en0106">3 types and above</td><td id="en0107">40</td><td id="en0108">20</td></tr></tbody></table></div><figcaption><span class="heading">Table 1</span><div id="spara010" role="paragraph">Basic information of 200 patients.</div><div class="core-table-tools"><ul><li><a class="core-table-show-action" href="/action/showFullTableHTML?isHtml=true&tableId=tbl0001&pii=S2472-6303%2824%2900042-6" target="_blank">Open table in a new tab</a></li></ul></div></figcaption></figure></div><div id="para0036" role="paragraph">In <a id="crf0043" href="#tbl0001">Table 1</a>, the proportion of men and women among the 200 patients finally selected was relatively balanced, with the age mainly concentrated between 41 and 62 years old. During dialysis, most patients had two types of complications, accounting for 50 %; among the primary diseases, diabetes accounted for the largest proportion, with 30 %; very few patients came to dialysis centers for HD at their own expense, accounting for only 15 %. At the same time, the vast majority of patients had a dialysis period of more than 6 years.</div></section><section id="sec-3-2"><h3>3.2 Criteria for inclusion and deletion</h3><div id="para0037" role="paragraph">The following requirements must be met for inclusion: 1. Long-term hemodialysis in the hospital under investigation's hemodialysis center; 2. Diabetic nephropathy diagnosis met and condition in a stable phase; The following exclusion criteria must be met: 1. HD contraindications; 2. Malignant tumors, heart failure, and liver insufficiency; 3. Severe visual impairment or psychological impairment; 4. Serious illness or disability; 5. Recent major surgery. 3. Age greater than eighteen; 4. Regular hemodialysis for more than three months.</div></section><section id="sec-3-3"><h3>3.3 Research techniques</h3><div id="para0038" role="paragraph">Care for the control group was standard. Perform well in the areas of medicine, nutrition, psychology, and other nursing care while the patient is receiving dialysis. After the patient's dialysis is over, provide internal fistula care, water control, and other pertinent nursing instructions. Keep an eye on the group's intelligent nursing system-based continuity of care. Form a nursing team, and for two weeks, each member of the team will participate in methodical training. and carry out pertinent assessments following the course, after which you can formally start working if you pass. If you do not pass, you will keep training for your deficiencies until you do. Ask patients about any difficulties they are having with self-care, and then provide specific advice.</div></section><section id="sec-3-4"><h3>3.4 Statistical methods</h3><div id="para0039" role="paragraph">Data were processed with SPSS 22.0 statistical software, and measurement data were expressed as mean ± standard deviation (<i>x</i> ± <i>s</i>), <i>t</i>-test, and <i>P</i> < 0.05 was statistically significant.</div></section></section><section id="sec-4"><h2>4 Experimental results</h2><div id="para0040" role="paragraph">Taking the 200 patients selected above as experimental subjects, a control experiment was conducted on these 200 patients. The patients were divided into a control group and an experimental group. These two groups randomly selected 100 patients. The control group used self-efficacy methods to care for patients, while the experimental group used intelligent nursing systems to care for MHD patients. The two groups investigated the complications of these 200 patients after various periods of nursing treatment. This study mainly investigated complications such as hypotension, malnutrition, acute cardiac decline, hypoglycemia, and fistula, to investigate the impact of intelligent nursing systems on the complications of MHD patients. The specific experimental results are shown in <a id="crf0044" href="#fig0007">Fig. 7</a>.</div><div class="figure-wrap"><figure id="fig0007" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/e73b759d-0bec-41b7-8ed0-66103012fca4/main.assets/gr7.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 7:" src="/cms/10.1016/j.slast.2024.100160/asset/e73b759d-0bec-41b7-8ed0-66103012fca4/main.assets/gr7.jpg" height="477" width="679" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0007-title" class="figure__title"><span class="label figure__label">Fig. 7</span> <span class="figure__title__text">Comparison of MHD patients’ complication rates between two nursing treatments.</span></div></figcaption></figure></div><div id="para0041" role="paragraph">In <a id="crf0045" href="#fig0007">Fig. 7</a>A, using intelligent nursing systems allowed the concurrent rate of MHD patients to be below 14 %. In <a id="crf0046" href="#fig0007">Fig. 7</a>B, the concurrent rate of MHD patients using self-efficacy intervention methods was above 13 %. Among them, MHD patients using intelligent nursing systems had the highest incidence of hypoglycemia, with 13 %, but it was 5 % lower than using self-efficacy intervention; MHD patients using intelligent nursing systems had the lowest complication rate of acute cardiac decline, with 6 %, which was 9 % lower than using self-efficacy intervention. Using self-efficacy to intervene in MHD patients had the highest incidence of malnutrition, with 21 %, which was 10 % higher than using intelligent nursing systems; using self-efficacy to intervene in MHD patients had the lowest complication rate of hypotension, only 14 %, but it was still 6 % higher than using intelligent nursing systems. The two methods of nursing MHD patients had the greatest difference in complication rates in terms of fistula closure, with the use of intelligent nursing systems being 12 % lower than the use of self-efficacy. The above data indicated that using intelligent nursing systems can reduce the probability of complications for MHD patients. It is possible to conduct comprehensive testing of all aspects of the patient before and after dialysis and provide comprehensive care for the patient's body, thereby reducing the occurrence of complications.</div><div id="para0042" role="paragraph">The care of nursing staff is only a part of MHD treatment, and the level of self-care ability of MHD patients is also very important. The improvement of the self-care ability of MHD patients can help them increase their ability to monitor disease status. Using intelligent nursing systems can improve the self-care ability of MHD patients, mainly in five aspects: self-concept awareness, self-care responsibility, self-care awareness, self-care skills, and health knowledge level. The five aspects were numbered 1, 2, 3, 4, and 5 in this article. To improve the self-care ability of MHD patients more significantly improved by intelligent nursing systems, a comparative experiment was conducted with self-efficacy intervention. After a period of experiment, the patient was scored, with a total score of 20 points for each aspect. The specific score is shown in <a id="crf0047" href="#fig0008">Fig. 8</a>.</div><div class="figure-wrap"><figure id="fig0008" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/7a75d029-9206-47ac-abab-3138dbe287ee/main.assets/gr8.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 8:" src="/cms/10.1016/j.slast.2024.100160/asset/7a75d029-9206-47ac-abab-3138dbe287ee/main.assets/gr8.jpg" height="455" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0008-title" class="figure__title"><span class="label figure__label">Fig. 8</span> <span class="figure__title__text">Comparison of the scores of two nursing treatment methods for improving the nursing ability of MHD patients.</span></div></figcaption></figure></div><div id="para0043" role="paragraph">According to <a id="crf0048" href="#fig0008">Fig. 8</a>, using intelligent nursing systems can score higher for improving the ability to care for MHD patients. In <a id="crf0049" href="#fig0008">Fig. 8</a>A, the intelligent nursing system scored above 16 points in all aspects of MHD patients’ nursing ability; in <a id="crf0050" href="#fig0008">Fig. 8</a>B, the scores of various aspects of self-efficacy intervention were below 15 points. Among them, the intelligent nursing system had the highest score for MHD patients in terms of self-care skills, with a score of 18.45 points, which was 4.65 points higher than the self-efficacy intervention score; for MHD patients, the intelligent nursing system scored the lowest on self-care awareness with number 3, only 16.3 points, but still 2.05 points higher than the self-efficacy intervention score. For MHD patients, self-efficacy intervention had the lowest score in number 5 health knowledge level, only 12.4 points, which was 4.65 points lower than the score of self intelligent nursing system; for MHD patients, self-efficacy intervention scored the highest on number 2, with a score of 14.75, but it was still 2.8 points lower than the score of intelligent nursing systems. The difference between the two nursing methods in MHD patients’ self-concept cognition scores on number 1 was the largest, and the intelligent nursing system scored 4.7 points higher than the self-efficacy intervention score. From the above data, it can be seen that using intelligent nursing systems can help MHD patients improve their ability to self-care. It allows patients to actively monitor their weight, blood pressure, and urine volume in daily life, and to properly mix food, thereby managing their ability to consume liquids.</div><div id="para0044" role="paragraph">This article surveyed 200 selected patients. The aspect of the survey was their satisfaction with the nursing effect before and after the use of the intelligent nursing system. The main evaluation indicators were divided into four levels, namely, very satisfied, satisfied, dissatisfied, and very dissatisfied. The specific MHD patient nursing satisfaction is shown in <a id="crf0051" href="#fig0009">Fig. 9</a>.</div><div class="figure-wrap"><figure id="fig0009" class="graphic"><a class="icon-full-screen" href="/cms/10.1016/j.slast.2024.100160/asset/eea90eb2-e624-4aa4-96c6-d815758e99fa/main.assets/gr9.jpg" target="_blank" title="View full size image in a new tab"><img alt="Fig. 9:" src="/cms/10.1016/j.slast.2024.100160/asset/eea90eb2-e624-4aa4-96c6-d815758e99fa/main.assets/gr9.jpg" height="454" width="678" loading="lazy" /></a><figcaption class="figure__caption__body"><div id="fig0009-title" class="figure__title"><span class="label figure__label">Fig. 9</span> <span class="figure__title__text">Comparison of MHD patients’ satisfaction with nursing outcomes before and after the use of intelligent nursing systems.</span></div></figcaption></figure></div><div id="para0045" role="paragraph">According to <a id="crf0052" href="#fig0009">Fig. 9</a>, after using intelligent nursing systems to care for MHD patients, the satisfaction of MHD patients with nursing effectiveness increased. In <a id="crf0053" href="#fig0009">Fig. 9</a>A, the total number of satisfied MHD patients after use was 185, with a total satisfaction rate of 92.5 %; in <a id="crf0054" href="#fig0009">Fig. 9</a>B, the total number of satisfied MHD patients before use was only 45, and the total satisfaction rate was only 22.5 %. After using the intelligent nursing system, the total number of patients with satisfaction increased by 140, and the overall satisfaction rate increased by 70 %. The total number of dissatisfied MHD patients after use was 15, with a total dissatisfaction rate of 7.5 %; the total number of patients with MHD before use was 155, with a total dissatisfaction rate of 77.5 %. The biggest difference between before and post-use levels was very dissatisfied, with 80 fewer patients being very dissatisfied after use than before. Using intelligent nursing systems, patients and their families can fully recognize the role of HD treatment in effectively prolonging the patient's life, thereby ensuring the patient's treatment compliance and self-management ability. This allows patients to strive to maintain a balanced diet and strictly limit sodium intake, thereby reducing the likelihood of complications and improving patient satisfaction.</div></section><section id="sec-5"><h2>5 Discussion</h2><div id="para0046" role="paragraph">To provide each patient with a customized nursing plan, the intelligent nursing system can gather and evaluate a variety of patient data, including physiological indications, the effects of dialysis, lifestyle choices, etc. Patients' quality of life and the effectiveness of their treatment both benefit from this. The technology can track patients' health in real-time, identify possible issues early on, and take the necessary precautions to lower the chance of consequences. Additionally, the intelligent nursing system offers case management and monitoring, allowing for the appropriate guidance of patients with low outpatient follow-up rates and limited mobility. This facilitates the monitoring of patients' MHD implementation and helps patients cope with the variety of issues that arise during dialysis, for patients to experience the changes that HD brings about, progressively increase adherence to treatment and capacity for self-management, to quicken the disease's healing process, to encourage an early return of patients to society, and to consistently enhance the patient's quality of life. Hospitals can better meet patient demands by allocating medical resources, such as medical staff and dialysis equipment, through the use of big data analysis. Large volumes of data can be automatically processed and analyzed by the intelligent nursing system, which can decrease manual labor and increase productivity. To help patients better grasp their disease and available treatments, the intelligent nursing system can offer individualized health education. Through the system, patients may keep an eye on their health and make timely adjustments to their lifestyle choices and treatment regimens, which will help them become more adept at managing their care.</div><div id="para0047" role="paragraph">Intelligent nursing systems have significant hurdles, including data security and privacy protection. Strict data protection procedures must be implemented by hospitals and developers to guarantee that patient information is kept private. As technology advances further, big data-driven intelligent nursing systems are anticipated to become more prevalent in the future, offering patients receiving maintenance hemodialysis more thorough and effective nursing care. In conclusion, there are a lot of opportunities and tremendous potential for the use of intelligent nursing systems based on big data in patients receiving maintenance hemodialysis. We may anticipate this technology to aid patient treatment and rehabilitation even more as it continues to optimize and improve system functionalities.</div></section><section id="sec-6"><h2>6 Conclusions</h2><div id="para0048" role="paragraph">With this shift in medical care philosophy, the goal of MHD intervention for patients with end-stage renal disease is no longer just to prolong their survival time, but to focus on improving their quality of life. However, MHD has the characteristics of a relatively long treatment process and multiple complications, which may cause patients to suffer from both physical and psychological attacks. This requires joint efforts from various aspects to improve the quality of life of patients and improve the actual effectiveness of nursing. Therefore, this article built an intelligent nursing system based on big data and applied it to the nursing practice of MHD patients. This would help nursing staff fully, deeply, and effectively participate in the entire process of patient follow-up treatment and rehabilitation, and establish a good medical relationship with the patient during hospitalization, thereby helping the patient to accept continuous nursing management with high trust and a positive attitude. Thus, it can effectively improve the patient's ability to self-care and improve their satisfaction with nursing. However, due to time and manpower constraints, the evaluation system studied in this paper is relatively single, and a more comprehensive evaluation system needs to be further established. The research time is too short and there are certain limitations in promotion. At the same time, the follow-up effect of the research plan is observed, and its long-term effect cannot be observed.</div></section><section id="sec-7"><h2>CRediT authorship contribution statement</h2><div id="para0048a" role="paragraph"><b>Qijun Long:</b> Formal analysis. <b>Meichun Yang:</b> Conceptualization. <b>Jiahui Long:</b> Investigation.</div></section><section id="conflict" class="core-conflict"><h2>Declaration of competing interest</h2><div id="para0049" role="paragraph">The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.</div></section><section id="acknowledgments" role="doc-acknowledgments"><h2>Funding</h2><div id="para0050" role="paragraph">This work was supported by Baise Scientific Research and Technology Development Plan Project 2022, project number: bike 20222925; 2023 Guangxi University Young and middle-aged Teachers Basic Research Ability Improvement project (Natural science), project number: 2023KY0578.</div></section></div></section><div class="core-linked-content"></div><section id="backmatter" data-extent="backmatter"><div class="core-container"><section id="references"><h2>References</h2><div id="bibliography" role="doc-bibliography"><div role="list"><div role="listitem" data-has="label"><div id="bib0001" class="citations"><div id="sbref0001" class="citation"><div class="citation-content"><div class="label">1.</div><div>Zhang, Z.</div><div><strong>Effects of expanded hemodialysis with medium cut-off membranes on maintenance hemodialysis patients: a review</strong></div><em>Membranes.</em> 2022; 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China, in 1983. He received his Bachelor's degree in Medicine from Youjiang Medical University of Nationalities and his master's and doctoral degrees from Philippine Christian University. He is currently working in Youjiang Medical University for Nationalities. His research interests include the international healthcare system, clinical medicine, international nursing management, death education, pathogenesis and treatment of Alzheimer's disease, and research on heavy metal toxicants.</div><div id="spara013" role="paragraph">E-mail: longqijun248@163.com</div></div><div id="auth2Bio2" class="core-biography"><img src="/cms/10.1016/j.slast.2024.100160/asset/d8d97132-e083-4751-bc71-f028421e10cd/main.assets/fx2.jpg" height="151" width="113" loading="lazy" /><div id="spara014" role="paragraph"><b>Meichun Yang</b> was born in Debao, Guangxi, P.R. China, in 1988. She received her Bachelor's degree in Medicine from Youjiang Medical University of Nationalities and her Master's degree in Nursing from Philippine Christian University. She is currently studying for a PhD in Nursing at the Philippine Christian University. Her research interests include clinical nursing, international nursing management, maintenance hemodialysis, and hospice care.</div><div id="spara015" role="paragraph">E-mail: Ymc18778699339@163.com</div></div><div id="auth3Bio3" class="core-biography"><img src="/cms/10.1016/j.slast.2024.100160/asset/03b6f6be-e0ec-4e98-b17c-c4d82bdc3e6a/main.assets/fx3.jpg" height="151" width="113" loading="lazy" /><div id="spara016" role="paragraph"><b>Jiahui Long</b> was born in Kaili, Guizhou, P.R. China, in 2004. She is currently studying at the Medical School of Xinjiang University of Science & Technology. Her main research interests are clinical nursing and hospice care.</div><div id="spara017" role="paragraph">E-mail: 15,086,236,222@163.com</div></div></section></div></section><div class="core-linked-content"> </div></div></div><div class="core-collateral-aside"><section id="core-collateral-figures"></section> <section id="core-collateral-metrics" class="publication-list__axel-section"><h2>Article metrics</h2><div><ul class="publication-list rlist publication-list--list plum-x--lazy-load publication-list--axel" 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class="heading"><span property="givenName">Qijun</span> <span property="familyName">Long</span></div><div class="content"><div class="core-author-affiliations"><div class="label">Affiliations</div><div property="affiliation" typeof="Organization"><span property="name">School of Public Health and Administration, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div><div property="affiliation" typeof="Organization"><span property="name">College of Innovation and Entrepreneurship, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div></div><div class="core-author-link"><a href="/authored-by/Long/Qijun" target="_blank">Search for articles by this author</a></div></div></div><div id="au0002" property="author" typeof="Person"><div class="heading"><span property="givenName">Meichun</span> <span property="familyName">Yang</span></div><div class="content"><div class="core-author-emails"><div class="label">Correspondence</div><div>Corresponding author.</div><div><a id="ead0001" href="mailto:Ymc18778699339@163.com" property="email" title="Send email to Meichun Yang">Ymc18778699339@163.com</a></div></div><div class="core-author-affiliations"><div class="label">Affiliations</div><div property="affiliation" typeof="Organization"><span property="name">Affiliated Hospital, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China</span></div><div property="affiliation" typeof="Organization"><span property="name">College for International Education, Philippine Christian University, Manila 1004, Philippines</span></div></div><div class="core-author-link"><a href="/authored-by/Yang/Meichun" target="_blank">Search for articles by this author</a></div></div></div><div id="au0003" property="author" typeof="Person"><div class="heading"><span property="givenName">Jiahui</span> <span property="familyName">Long</span></div><div class="content"><div 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Application of intelligent nursing system based on big data in maintenance hemodialysis patients - SLAS Technology