{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:53Z","timestamp":1755219833369,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>In Japan, chat-based mental health counseling services have low response rates due to understaffing. In this article, machine learning (ML) based suicide risk classification methods are proposed. A dataset was constructed including a medical questionnaire (MQ) and open-ended consultation text (CT) as preliminary information, chat logs, and six-level risk assessments. Among the five methods, M3, which output intermediate predictions separately for MQ and CT, achieved the highest ROC-AUC (0.879). Classification results indicate that it is better to use both MQ and CT rather than MQ or CT alone. Among the items in MQ, the most important item was suicidal ideation. Although some cases remained challenging to classify, the proposed methods effectively prioritized high-risk users.<\/jats:p>","DOI":"10.3233\/shti250962","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:26Z","timestamp":1754566586000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine-Learning-Based Prediction of Suicide Risk Using Preliminary Questionnaire and Consultation Text"],"prefix":"10.3233","author":[{"given":"Ryota","family":"Ogasawara","sequence":"first","affiliation":[{"name":"Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeshi","family":"Imai","sequence":"additional","affiliation":[{"name":"Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuyoshi","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Clinical Data Science, Clinical Research & Education Promotion Division, National Center of Neurology and Psychiatry Hospital, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuyuki","family":"Nakagome","sequence":"additional","affiliation":[{"name":"National Center of Neurology and Psychiatry Hospital, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:26Z","timestamp":1754566586000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250962","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}