Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.

IF 2.7 3区 医学 Q2 PSYCHIATRY
Suicide and Life-Threatening Behavior Pub Date : 2024-06-01 Epub Date: 2024-02-12 DOI:10.1111/sltb.13056
Meytal Grimland, Joy Benatov, Hadas Yeshayahu, Daniel Izmaylov, Avi Segal, Kobi Gal, Yossi Levi-Belz
{"title":"Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.","authors":"Meytal Grimland, Joy Benatov, Hadas Yeshayahu, Daniel Izmaylov, Avi Segal, Kobi Gal, Yossi Levi-Belz","doi":"10.1111/sltb.13056","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, we aimed to understand the specific theory-driven factors contributing to the ML prediction of suicide risk.</p><p><strong>Method: </strong>The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors-based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk-Bert; SR-BERT) was trained using natural language processing techniques incorporating the SRF lexicon.</p><p><strong>Results: </strong>The results showed that SR-BERT outperformed the other models. Logistic regression analysis identified several theory-driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self-harm, and thwarted belongingness.</p><p><strong>Limitations: </strong>The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non-professionals in metal health.</p><p><strong>Conclusion: </strong>This study highlights the potential of how ML models combined with theory-driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention.</p>","PeriodicalId":39684,"journal":{"name":"Suicide and Life-Threatening Behavior","volume":" ","pages":"416-424"},"PeriodicalIF":2.7000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Suicide and Life-Threatening Behavior","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/sltb.13056","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/12 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, we aimed to understand the specific theory-driven factors contributing to the ML prediction of suicide risk.

Method: The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors-based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk-Bert; SR-BERT) was trained using natural language processing techniques incorporating the SRF lexicon.

Results: The results showed that SR-BERT outperformed the other models. Logistic regression analysis identified several theory-driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self-harm, and thwarted belongingness.

Limitations: The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non-professionals in metal health.

Conclusion: This study highlights the potential of how ML models combined with theory-driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention.

在实时危机热线聊天中预测自杀风险,将机器学习与心理因素相结合:探索黑匣子
研究背景本研究通过探索在实时危机热线聊天中整合了理论驱动的心理风险因素的机器学习(ML)模型的预测能力,来解决自杀风险预测难题。更重要的是,我们旨在了解有助于ML预测自杀风险的具体理论驱动因素:数据集由 17654 个危机热线聊天会话组成,这些会话被二分法归类为有自杀倾向或无自杀倾向。我们创建了一个基于自杀风险因素的词典(SRF),其中包含从主要自杀理论中得出的关键风险因素的语言表述。我们使用自然语言处理技术结合 SRF 词库对 ML 模型(Suicide Risk-Bert; SR-BERT)进行了训练:结果表明,SR-BERT 的表现优于其他模型。逻辑回归分析确定了几个与自杀风险显著相关的理论驱动风险因素,其中最突出的是绝望、自杀史、自残和归属感受挫:词汇表的能力有限,既不能完全涵盖与自杀风险相关的所有理论概念,也不能涵盖每个概念的所有语言表达。聊天记录的分类是由经过培训的金属健康非专业人员确定的:本研究强调了 ML 模型与理论知识相结合可以改善自杀风险预测的潜力。我们的研究强调了绝望和归属感受挫在自杀风险中的重要性,以及它们在自杀预防和干预中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Suicide and Life-Threatening Behavior
Suicide and Life-Threatening Behavior Medicine-Public Health, Environmental and Occupational Health
CiteScore
5.40
自引率
3.10%
发文量
96
期刊介绍: An excellent resource for researchers as well as students, Social Cognition features reports on empirical research, self-perception, self-concept, social neuroscience, person-memory integration, social schemata, the development of social cognition, and the role of affect in memory and perception. Three broad concerns define the scope of the journal: - The processes underlying the perception, memory, and judgment of social stimuli - The effects of social, cultural, and affective factors on the processing of information - The behavioral and interpersonal consequences of cognitive processes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信