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<!-- views/paperById.ejs --> <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS</title> <meta name ="description" content="Digital Library" /> <meta name="citation_language" content="en"> <meta name="citation_title" content="Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App"> <meta name="citation_abstract" content="Machine learning (ML) integrated with technology has been a breakthrough in mental health, bringing clinical improvements both for the patient and for the clinician. Among these, real-time patient symptoms’ tracking through ecological momentary assessment (EMA) data can be a valuable tool to forecast symptomatology at the individual-patient level for specific disorders, among which non suicidal self-injury. We aimed at applying classification trees to predict non-suicidal self-injury (NSSI) with EMA data collected through a mobile app. A database of 40 patients diagnosed with borderline personality disorder (BPD) with NSSI (N=22), and a subclinical group of students with NSSI (N=19) was analysed. EMA data was collected by the Sinjur app. Two classification trees were used as models. For the first tree, training results reported 69,7% of accuracy, whereas test results reported 59,3% of accuracy, 87,5% of sensitivity and 58,78% of specificity. For the second tree, training results reported 67,9% of accuracy, whereas test results reported 65,2% of accuracy, 85% of sensitivity and 64,8% of specificity. We concluded that real-time patient monitoring via a mobile app can be a valuable tool for making technology-based predictions at the individual patient level. This promising data needs to be built upon in future studies and needs major translation in the everyday clinical practice to demonstrate its real-world efficacy and later, to be translated to the enterprise world."> <meta name="citation_publication_date" content="2022/02/09"> <meta name="citation_conference_title" content="International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)"> <meta name="citation_keywords" content="Machine Learning; Digital Mental Health; Non-suicidal Self-injury; Applied AI;"> <meta name="citation_doi" content="10.5220/0010978500003123"> <meta name="citation_isbn" content="978-989-758-552-4"> <meta name="citation_volume" content="2"> <meta name="citation_firstpage" content="278"> <meta name="citation_lastpage" content="282"> <meta name="citation_publisher" content="SCITEPRESS"> <meta name="citation_author" content="Chiara Capra" > <meta name="citation_author_institution" content="beHIT, carrer de Mata 1, Barcelona, Spain" > <meta name="citation_author" content="Chiara Capra" > <meta name="citation_author_institution" content="Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain" > <meta name="citation_author" content="Pere Marti-Puig" > <meta name="citation_author_institution" content="Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain" > <meta name="citation_author" content="Daniel Vega Moreno" > <meta name="citation_author_institution" content="Department of Psychiatry & Mental Health, Hospital d’Igualada, Fundació Sanitària Igualada, Igualada, Spain" > <meta name="citation_author" content="Laia Llunas" > <meta name="citation_author_institution" content="beHIT, carrer de Mata 1, Barcelona, Spain" > <meta name="citation_author" content="Stella Nicolaou" > <meta name="citation_author_institution" content="Department of Psychiatry & Mental Health, Hospital d’Igualada, Fundació Sanitària Igualada, Igualada, Spain" > <meta name="citation_author" content="Carlos Schmidt" > <meta name="citation_author_institution" content="Department of Psychiatry & Mental Health, Hospital d’Igualada, Fundació Sanitària Igualada, Igualada, Spain" > <meta name="citation_author" content="Jordi Solé-Casals" > <meta name="citation_author_institution" content="Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain" > <meta name="citation_abstract_html_url" content="/PublishedPapers/2022/109785"> <meta name="citation_pdf_url" content="/PublishedPapers/2022/109785/109785.pdf"> </head> <body> <article> <a href="/publishedPapers/2022/109785/pdf/index.html"><h1 class="citation_title">Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App</h1></a> <h3 class="citation_author"> Chiara Capra, Chiara Capra, Pere Marti-Puig, Daniel Vega Moreno, Laia Llunas, Stella Nicolaou, Carlos Schmidt, Jordi Solé-Casals</h3> <h4 class="citation_publication_date">2022</h4> <h4>Abstract</h4> <p class="citation_abstract">Machine learning (ML) integrated with technology has been a breakthrough in mental health, bringing clinical improvements both for the patient and for the clinician. Among these, real-time patient symptoms’ tracking through ecological momentary assessment (EMA) data can be a valuable tool to forecast symptomatology at the individual-patient level for specific disorders, among which non suicidal self-injury. We aimed at applying classification trees to predict non-suicidal self-injury (NSSI) with EMA data collected through a mobile app. A database of 40 patients diagnosed with borderline personality disorder (BPD) with NSSI (N=22), and a subclinical group of students with NSSI (N=19) was analysed. EMA data was collected by the Sinjur app. Two classification trees were used as models. For the first tree, training results reported 69,7% of accuracy, whereas test results reported 59,3% of accuracy, 87,5% of sensitivity and 58,78% of specificity. For the second tree, training results reported 67,9% of accuracy, whereas test results reported 65,2% of accuracy, 85% of sensitivity and 64,8% of specificity. We concluded that real-time patient monitoring via a mobile app can be a valuable tool for making technology-based predictions at the individual patient level. This promising data needs to be built upon in future studies and needs major translation in the everyday clinical practice to demonstrate its real-world efficacy and later, to be translated to the enterprise world.</p> <a href="/PublishedPapers/2022/109785/109785.pdf" class="citation_pdf_url">Download</a> <br /> <br /> <br/> <h4 style="margin:0;">Paper Citation</h4> <br/> <h4 style="margin:0;">in Harvard Style</h4> <p style="margin:0;">Capra C., Marti-Puig P., Moreno D., Llunas L., Nicolaou S., Schmidt C. and Solé-Casals J. (2022). <b>Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App</b>. In <i>Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS</i>; ISBN 978-989-758-552-4, SciTePress, pages 278-282. DOI: 10.5220/0010978500003123</p> <br/> <h4 style="margin:0;">in Bibtex Style</h4> <p style="margin:0;">@conference{biosignals22,<br />author={Chiara Capra and Pere Marti-Puig and Daniel Vega Moreno and Laia Llunas and Stella Nicolaou and Carlos Schmidt and Jordi Solé-Casals},<br />title={Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App},<br />booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS},<br />year={2022},<br />pages={278-282},<br />publisher={SciTePress},<br />organization={INSTICC},<br />doi={10.5220/0010978500003123},<br />isbn={978-989-758-552-4},<br />}</p> <br/> <h4 style="margin:0;">in EndNote Style</h4> <p style="margin:0;">TY - CONF <br /><br />JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS<br />TI - Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App<br />SN - 978-989-758-552-4<br />AU - Capra C. <br />AU - Marti-Puig P. <br />AU - Moreno D. <br />AU - Llunas L. <br />AU - Nicolaou S. <br />AU - Schmidt C. <br />AU - Solé-Casals J. <br />PY - 2022<br />SP - 278<br />EP - 282<br />DO - 10.5220/0010978500003123<br />PB - SciTePress<br /></p> <br/> </article> </body> </html>