Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"/> <meta content="width=device-width, initial-scale=1" name="viewport"/> <meta content="IE=edge" http-equiv="X-UA-Compatible"/> <title>Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data | WashU McKelvey School of Engineering</title> <link href="https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html" rel="canonical"/> <link href="https://use.typekit.net/zts4tuf.css" rel="stylesheet"/> <link fetchpriority="high" href="../../_files/css/rebrand-min.css" rel="stylesheet" type="text/css"/> <link href="../../_files/images/favicon.ico" rel="icon" sizes="48x48"/> <link href="../../_files/images/shield_favicon_512.png" rel="icon" sizes="any" type="image/png"/> <link crossorigin="anonymous" href="https://www.googletagmanager.com" rel="preconnect"/> <link href="https://siteimproveanalytics.com" rel="preconnect"/> <script>document.documentElement.className = 'js';</script> <meta content="Chenyang Lu" name="fb_faculty"/> <link href="https://mckelveyengineeringnews.imgix.net" rel="preconnect"/> <meta content="cse" name="fb_department"/> <meta content="Computer Science & Engineering" name="fb_department_friendly"/> <meta content="Graduate Students" name="fb_audience"/> <meta content="Research" name="fb_audience"/> <meta content="Wed Nov 03 00:00:00 CDT 2021" name="fb_date"/> <meta content="Beth Miller " name="fb_author"/> <meta content="bd16f903ac1e003b65a0572da288ff19" name="id"/> <meta content="article" property="og:type"/> <meta content="@WashUengineers" name="twitter:site"/> <meta content="summary_large_image" name="twitter:card"/> <meta content="Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data" property="og:title"/> <meta content="The model, developed in Chenyang Lu鈥檚 lab, may help warn of pending patient deterioration " name="description" property="og:description"/><meta content="https://engineering.washu.edu/news/images/imgix/iStock-1043174832.jpeg" property="og:image"/><meta content="A multi-disciplinary team of researchers at Washington University in St. Louis is developing a machine-learning-based early warning system model to predict this deterioration and improve patient outcomes. (iStock photo)" name="twitter:image:alt"/><meta content="News" name="fb_tab"/><meta content="https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html" property="og:url"/> <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "NewsArticle", "headline": "Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data", "image": [ "https://engineering.washu.edu/news/images/imgix/iStock-1043174832.jpeg" ], "datePublished": "2021-10-26T09:52-06:00", "dateModified": "2022-02-23T14:15-06:00" } </script> <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); 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<li><a href="../../academics/summer-school.html">summer school</a></li> </ul> </div> </div> </div> </div> </header> <main class="layout" id="swup"> <article aria-labelledby="news1" class="news-single "> <header class="page_top_header images-1 position-right"> <nav aria-label="breadcrumbs" class="breadcrumbs"> <ol typeof="BreadcrumbList" vocab="https://schema.org/"> <li property="itemListElement" typeof="ListItem"><a href="../../index.html" property="item" typeof="WebPage"><span property="name">Home</span></a><meta content="1" property="position"/></li> <li property="itemListElement" typeof="ListItem"><a href="../index.html" property="item" typeof="WebPage"><span property="name">News & Events</span></a><meta content="2" property="position"/></li> <li aria-current="page" property="itemListElement" typeof="ListItem"><span property="name">Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data</span><meta content="3" property="position"/></li> </ol> </nav> <div class="page-header__text container"> <h1 class="page-header__headline" id="news1">Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data</h1> <p class="page-header__desc">The model, developed in Chenyang Lu鈥檚 lab, may help warn of pending patient deterioration</p> <div class="news__meta"> Beth Miller 聽 <time class="news__date" datetime="2021-11-03">11.03.2021</time> </div> </div> <div class="page-header__image"> <figure> <img alt="A multi-disciplinary team of researchers at Washington University in St. Louis is developing a machine-learning-based early warning system model to predict this deterioration and improve patient outcomes. (iStock photo)" height="400" src="https://mckelveyengineeringnews.imgix.net/iStock-1043174832.jpeg?w=600&h=400&auto=format&q=35&cs=tinysrgb&fit=crop&dpr=2" width="600"/> <figcaption class="module__figcaption">A multi-disciplinary team of researchers at Washington University in St. Louis is developing a machine-learning-based early warning system model to predict this deterioration and improve patient outcomes. (iStock photo)</figcaption> </figure> </div> </header> <section class="news-single__content"> <div class="sticky__share share__buttons"> <p class="share__title">Share</p> <a href="https://www.facebook.com/sharer/sharer.php?u=https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html" rel="noopener" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" enable-background="new 0 0 24 24" height="24px" viewBox="0 0 24 24" width="24px" x="0px" xml:space="preserve" xmlns:xlink="http://www.w3.org/1999/xlink" y="0px"><path d="M20.1,2.9H3.9c-0.6,0-1,0.4-1,1v16.1c0,0.6,0.4,1,1,1h8.7v-7h-2.4v-2.7h2.4v-2c-0.2-1.8,1.1-3.4,3-3.6c0.2,0,0.4,0,0.6,0c0.7,0,1.4,0,2.1,0.1v2.4h-1.4c-1.1,0-1.4,0.5-1.4,1.3v1.7h2.7L17.8,14h-2.4v7h4.6c0.6,0,1-0.4,1-1V3.9C21.1,3.4,20.6,2.9,20.1,2.9z" id="Facebook"/></svg><span class="sr-only">Share on </span>Facebook</a> <a href="https://twitter.com/intent/tweet?url=https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html&via=WashUengineers&text=Check%20out%20this%20news%20story" rel="noopener" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" height="21" viewBox="0 0 21 21" width="21"><path d="m12.496094 8.886719 7.820312-8.886719h-1.851562l-6.789063 7.71875-5.421875-7.71875h-6.253906l8.199219 11.667969-8.199219 9.320312h1.851562l7.167969-8.148437 5.726563 8.148437h6.253906zm-2.535156 2.886719-.832032-1.164063-6.609375-9.246094h2.847657l5.332031 7.460938.832031 1.164062 6.933594 9.699219h-2.847656zm0 0"/></svg><span class="sr-only">Share on </span>Twitter</a> <a href="https://www.linkedin.com/shareArticle?mini=true&url=https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html" rel="noopener" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" enable-background="new 0 0 24 24" height="24px" viewBox="0 0 24 24" width="24px" x="0px" xml:space="preserve" xmlns:xlink="http://www.w3.org/1999/xlink" y="0px"><path d="M3.6,9.1h3.6v11.6H3.6V9.1z M5.4,3.3c1.2,0,2.1,0.9,2.1,2.1S6.6,7.5,5.4,7.5c-1.2,0-2.1-0.9-2.1-2.1c0,0,0,0,0,0C3.3,4.3,4.3,3.3,5.4,3.3C5.4,3.3,5.4,3.3,5.4,3.3"/><path d="M9.5,9.1h3.4v1.6h0c0.7-1.2,2-1.9,3.4-1.9c3.6,0,4.3,2.4,4.3,5.5v6.3h-3.6V15c0-1.3,0-3.1-1.9-3.1c-1.9,0-2.2,1.5-2.2,3v5.7H9.5L9.5,9.1z"/></svg><span class="sr-only">Share on </span>Linkedin</a> <a href="mailto:?subject=Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data&body=https://engineering.washu.edu/news/2021/Early-warning-system-model-predicts-deterioration-of-hospitalized-cancer-patients-based-on-clinical-data.html" rel="noopener" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" height="12" viewBox="0 0 16 12" width="16"><path d="M14.5-12H1.5A1.5,1.5,0,0,0,0-10.5v9A1.5,1.5,0,0,0,1.5,0h13A1.5,1.5,0,0,0,16-1.5v-9A1.5,1.5,0,0,0,14.5-12Zm0,1.5v1.275c-.7.571-1.818,1.458-4.206,3.328C9.768-5.483,8.725-4.489,8-4.5c-.725.012-1.768-.983-2.294-1.4C3.318-7.767,2.2-8.654,1.5-9.225V-10.5Zm-13,9V-7.3c.716.57,1.732,1.371,3.279,2.583C5.462-4.18,6.658-2.993,8-3s2.516-1.162,3.22-1.717C12.768-5.929,13.784-6.73,14.5-7.3v5.8Z" data-name="Path 14725" id="Path_14725" transform="translate(0 12)"/></svg>Email</a> </div> <div class="news-single__content-main wysiwyg"> <p>About 9% of cancer patients experience complications while hospitalized that lead to a deterioration in their condition, a transfer to the intensive care unit or even death. A multi-disciplinary team of researchers at Washington University in St. Louis is developing a machine-learning-based early warning system model to predict this deterioration and improve patient outcomes.</p> <p>Chenyang Lu, the Fullgraf Professor in the McKelvey School of Engineering, with collaborators including Marin Kollef, MD, the Golman Professor of Medicine and director of the medical intensive care unit and respiratory care services at Barnes-Jewish Hospital, and Patrick Lyons, MD, instructor in medicine in the School of Medicine, recently developed a new predictive model for hospitalized cancer patients that integrates heterogeneous data available in electronic health records (EHR). Results of their work were presented at the Association for Computing Machinery (ACM) Conference on Information and Knowledge Management (CIKM) Nov. 3, 2021.</p> <p>Using historical, de-identified data from more than 20,000 hospitalizations of patients with cancer at Barnes-Jewish Hospital, Lu and Dingwen Li, a doctoral student his lab and first author of the paper, found a way to integrate two types of valuable data in deep learning models that might offer clues about a patient’s condition: static data, or data collected at the time of admission, such as demographics, other medical diagnoses or information from previous hospitalizations; and time-series data, which is collected repeatedly during a hospital stay and includes body temperature, blood pressure, medication and test results. As static and time-series data contain complementary information related to clinical deterioration, it is important for a predictive model to exploit both types of variables to maximize its accuracy, Lu said.</p> <p>“There are early signs hidden in the data that suggest that a person will develop clinical deterioration in a few hours or a few days,” said Lu, an expert in the Internet of Things, cyber-physical systems and clinical artificial intelligence. “Humans cannot see these hidden patterns or trends in the data, so this is where machine learning is very good at picking up these patterns.”</p> <p>Lu and his team used a recurrent neural network (RNN) model that originally was designed for the time-series data and enhanced it to incorporate the static data using a multi-modal fusion approach. Their end-to-end model, called CrossNet, learns how to predict deterioration events while accurately imputing any missing static or time-series data. This novel approach to in- corporate both static and time-series data combines the power of deep recurrent models and the benefits of heterogeneous data in EHR.</p> <p>Ideally, an early warning system would learn from a patient’s data the signs that the patient is deteriorating, which would activate an alarm calling health care providers to the bedside. However, one of the risks with such a system is that an alarm would sound so frequently, possibly triggered by false alarms, that the health care providers would develop alarm fatigue and eventually stop responding. In a case study under realistic inpatient care settings Lu and the team set a threshold of 48 notifications in a 24-hour period, or one every 30 minutes. The team then implemented a more proactive early warning system where the alarm rate can be high but the number of false alarms is limited to avoid alarm fatigue. With the same false alarm rate, the team’s CrossNet model captured 39.52% of clinical deterioration events, while an existing model used by many hospitals called the Modified Early Warning Scores (MEWS) captured only 3.92% of the same events.</p> <p>While the model has potential, Lu is working with the physicians on the team to determine the best way to implement it in a hospital setting. </p> <p>Kollef said Barnes-Jewish Hospital has been using a simpler early warning system for about 15 years. After some evaluation, alarms from that system are sent to an early response team that can assess and triage patients.</p> <p>“An alert is meaningless unless it’s tied to intervention,” Kollef said. “It’s easy for someone to pull data from a machine and analyze it, but what do you do with it? That’s the challenge.”</p> <p>Kollef, who has worked in the intensive care unit for 35 years, said an early warning system is a step in the right direction, and collaborators such as Lyons are key to implementing such a system.</p> <p>“Patients with cancer are often very sick and fragile and are already monitored intensely,” said Lyons, an informatics-based clinician. “With chemotherapy and other treatments, they generate a lot of data that is hard to sort through in a meaningful way. We would like to use this model to distill what data is going to point health care providers toward a clear direction.”</p> <p>Lyons said the team is seeking funding to build an infrastructure around their model and test it to see if it improves the processes of care. In the meantime, he is doing focus groups with patients and nurses to determine their priorities.</p> <hr/> <p>Li D, Lyons P, Klaus J, Gage B, Kollef M, Lu C. Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems. Proceedings of the 30<sup>th</sup> ACM International Conference on Information and Knowledge Management, Nov. 1-5, 2021. https://doi.org/10.1145/3459637.3482441</p> <hr/> <div class="news-boilerplate">The McKelvey School of Engineering at Washington University in St. Louis promotes independent inquiry and education with an emphasis on scientific excellence, innovation and collaboration without boundaries. McKelvey Engineering has top-ranked research and graduate programs across departments, particularly in biomedical engineering, environmental engineering and computing, and has one of the most selective undergraduate programs in the country. With 165 full-time faculty, 1,420 undergraduate students, 1,614 graduate students and 21,000 living alumni, we are working to solve some of society鈥檚 greatest challenges; to prepare students to become leaders and innovate throughout their careers; and to be a catalyst of economic development for the St. Louis region and beyond.</div> <h4>Click on the topics below for more stories in those areas</h4> <ul class="news__cats"> <li><a href="https://search.engineering.wustl.edu/s/search.html?collection=wustl-search&profile=engineering&f.Tabs%7CengNews=News&sort=date&f.Topic%7CengAudience=Graduate+Students&f.Audience%7CengAudience=Graduate+Students&query=%21showall">Graduate Students</a></li> <li><a href="https://search.engineering.wustl.edu/s/search.html?collection=wustl-search&profile=engineering&f.Tabs%7CengNews=News&sort=date&f.Topic%7CengAudience=Research&f.Audience%7CengAudience=Research&query=%21showall">Research</a></li> <li><a href="https://cse.washu.edu/index.html">Computer Science & Engineering</a></li> </ul> <a class="button" href="../index.html">Back to News</a> </div> <div class="sidebar news-single__content-aside"> <div class="wrapper sidebar__faculty"> <p class="module__title module__title--dots">Faculty in this story</p> <article aria-labelledby="faculty2" class="faculty-directory__teaser"> <a class="faculty-directory__teaser-container" href="../../faculty/Chenyang-Lu.html"> <div class="faculty-directory__teaser-image"> <img alt="Chenyang Lu" height="200" loading="lazy" src="https://mckelveyengineeringfaculty.imgix.net/Chenyang_Lu_2022.jpg?w=250&h=200&auto=format&cs=tinysrgb&fit=facearea&facepad=4&q=65" srcset="https://mckelveyengineeringfaculty.imgix.net/Chenyang_Lu_2022.jpg?w=250&h=200&auto=format&cs=tinysrgb&fit=facearea&facepad=4&q=40&dpr=2 2x" width="250"/> <span class="action">View Profile</span> </div> <h4 class="faculty-directory__teaser-name" id="faculty2">Chenyang Lu</h4> </a> <p class="faculty-directory__teaser-title">Professor</p> </article> <figure class="image"><img alt="kollef-250x200.jpg" class="" height="200" loading="lazy" src="../images/kollef-250x200.jpg" width="250"/> <figcaption> <h4 class="faculty-directory__teaser-name">Marin Kollef, MD</h4> <p class="faculty-directory__teaser-title">Golman Professor of Medicine<br/>Washington University School of Medicine</p> </figcaption> </figure> <p> </p> <figure class="image"><img alt="Lyons-1-250x200.jpg" height="200" loading="lazy" src="../images/Lyons-1-250x2001.jpg" width="250"/> <figcaption> <h4 class="faculty-directory__teaser-name">Patrick Lyons, MD</h4> <p class="faculty-directory__teaser-title">Instructor in Medicine<br/>Washington University School of Medicine</p> </figcaption> </figure> <p> </p> <figure class="image"><img alt="li,-dingwen-3.jpg" class="" height="200" loading="lazy" src="../images/li,-dingwen-3.jpg" width="250"/> <figcaption> <h4 class="faculty-directory__teaser-name">Dingwen Li</h4> <p class="faculty-directory__teaser-title">Doctoral student </p> </figcaption> </figure> <p> </p> <p>Hematology/Oncology Today: <a href="https://www.healio.com/news/hematology-oncology/20220221/model-predicts-deterioration-of-hospitalized-patients-with-cancer">Model predicts deterioration of hospitalized patients with cancer</a> </p> </div> </div> </section> </article>  <h3 class="module__headline container related-news__header">You may also be interested in:</h3> <div class="news__list container wrapper"> <article aria-labelledby="news3" class="news__teaser"> <a class="news__teaser-header" href="../2024/Converting-CO2-to-solid-carbon-yields-benefits-for-batteries-and-more.html" rel="bookmark"> <div class="news__teaser-image "> <img alt="A 3D rendering of a carbon nanotube. 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Early warning system model predicts deterioration of hospitalized cancer patients based on clinical data | WashU McKelvey School of Engineering