利用自传体记忆的自然语言处理预测自杀。

IF 2 3区 医学 Q2 PSYCHIATRY
Vithor Rosa Franco, Makilim Nunes Baptista, Giovana Aparecida Leopoldino
{"title":"利用自传体记忆的自然语言处理预测自杀。","authors":"Vithor Rosa Franco, Makilim Nunes Baptista, Giovana Aparecida Leopoldino","doi":"10.1080/13811118.2025.2552951","DOIUrl":null,"url":null,"abstract":"<p><p>Autobiographical memory, a critical cognitive process for recalling personal events, is closely linked to mental health. Depressive disorders are characterized by overgeneralized and negative memory patterns, which impair future-oriented thinking and exacerbate hopelessness. Current evaluations of autobiographical memory are subjective and limited by human bias. In this study, we applied Natural Language Processing using Large Language Models (LLMs) to analyze autobiographical memory narratives, uncovering that their valence can predict depression, suicidal ideation, and prior suicide attempts. Furthermore, valence correlated with core components of the Three-Step Theory of suicide, such as hopelessness and lack of connectedness. By integrating advanced computational techniques, our approach demonstrated high predictive accuracy and offers a scalable, objective method for assessing suicide risk. These findings highlight the potential of LLM-based analysis in enhancing psychological assessment and informing interventions, paving the way for innovative clinical applications in mental health care.</p>","PeriodicalId":8325,"journal":{"name":"Archives of Suicide Research","volume":" ","pages":"1-15"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Suicide Using Natural Language Processing of Autobiographical Memory.\",\"authors\":\"Vithor Rosa Franco, Makilim Nunes Baptista, Giovana Aparecida Leopoldino\",\"doi\":\"10.1080/13811118.2025.2552951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Autobiographical memory, a critical cognitive process for recalling personal events, is closely linked to mental health. Depressive disorders are characterized by overgeneralized and negative memory patterns, which impair future-oriented thinking and exacerbate hopelessness. Current evaluations of autobiographical memory are subjective and limited by human bias. In this study, we applied Natural Language Processing using Large Language Models (LLMs) to analyze autobiographical memory narratives, uncovering that their valence can predict depression, suicidal ideation, and prior suicide attempts. Furthermore, valence correlated with core components of the Three-Step Theory of suicide, such as hopelessness and lack of connectedness. By integrating advanced computational techniques, our approach demonstrated high predictive accuracy and offers a scalable, objective method for assessing suicide risk. These findings highlight the potential of LLM-based analysis in enhancing psychological assessment and informing interventions, paving the way for innovative clinical applications in mental health care.</p>\",\"PeriodicalId\":8325,\"journal\":{\"name\":\"Archives of Suicide Research\",\"volume\":\" \",\"pages\":\"1-15\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Suicide Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/13811118.2025.2552951\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Suicide Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/13811118.2025.2552951","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0

摘要

自传式记忆是回忆个人事件的关键认知过程,与心理健康密切相关。抑郁症的特点是过度概括和消极的记忆模式,损害了面向未来的思维,加剧了绝望。目前对自传式记忆的评价是主观的,受人类偏见的限制。在这项研究中,我们运用自然语言处理和大语言模型(LLMs)来分析自传体记忆叙事,发现它们的效价可以预测抑郁、自杀意念和先前的自杀企图。此外,效价与自杀三步理论的核心成分相关,如绝望和缺乏联系。通过整合先进的计算技术,我们的方法显示出很高的预测准确性,并提供了一种可扩展的、客观的自杀风险评估方法。这些发现突出了基于法学硕士的分析在加强心理评估和告知干预措施方面的潜力,为精神卫生保健的创新临床应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Suicide Using Natural Language Processing of Autobiographical Memory.

Autobiographical memory, a critical cognitive process for recalling personal events, is closely linked to mental health. Depressive disorders are characterized by overgeneralized and negative memory patterns, which impair future-oriented thinking and exacerbate hopelessness. Current evaluations of autobiographical memory are subjective and limited by human bias. In this study, we applied Natural Language Processing using Large Language Models (LLMs) to analyze autobiographical memory narratives, uncovering that their valence can predict depression, suicidal ideation, and prior suicide attempts. Furthermore, valence correlated with core components of the Three-Step Theory of suicide, such as hopelessness and lack of connectedness. By integrating advanced computational techniques, our approach demonstrated high predictive accuracy and offers a scalable, objective method for assessing suicide risk. These findings highlight the potential of LLM-based analysis in enhancing psychological assessment and informing interventions, paving the way for innovative clinical applications in mental health care.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
7.10%
发文量
69
期刊介绍: Archives of Suicide Research, the official journal of the International Academy of Suicide Research (IASR), is the international journal in the field of suicidology. The journal features original, refereed contributions on the study of suicide, suicidal behavior, its causes and effects, and techniques for prevention. The journal incorporates research-based and theoretical articles contributed by a diverse range of authors interested in investigating the biological, pharmacological, psychiatric, psychological, and sociological aspects of suicide.
×
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:604180095
Book学术官方微信