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}
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.
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.