Sedano-Capdevila Alba, Toledo-Acosta Mauricio, Barrigon María Luisa, Morales-González Eliseo, Torres-Moreno David, Martínez-Zaldivar Bolívar, Hermosillo-Valadez Jorge, Baca-García Enrique
Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain.
Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, 62209 Cuernavaca, Morelos, México.
Psychiatry Res. 2023 Apr;322:115090. doi: 10.1016/j.psychres.2023.115090. Epub 2023 Feb 5.
Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question "how are you feeling today?" were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients' free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.
传统的研究方法在自杀风险评估中显示出较低的预测价值，且在临床实践中的应用存在局限性。作者试图评估自然语言处理作为一种评估自我伤害性想法、行为及相关情绪的新工具。我们使用MEmind项目对2838名精神科门诊患者进行评估。根据患者的情绪状态，收集他们对开放式问题“你今天感觉如何？”的匿名非结构化回答。利用自然语言处理技术处理患者的文字记录。文本被自动呈现（语料库）并进行分析，以确定其情感内容和自杀风险程度。作者将患者的文本与一个用于评估求生欲望缺失的问题进行比较，以此作为自杀风险评估工具。语料库由5489份简短的自由文本文件组成，包含12256个分词或独特词汇。与求生欲望缺失问题的回答相比，自然语言处理的ROC-AUC评分为0.9638。自然语言处理在利用患者的自由文本根据其求生欲望缺失程度对受试者进行自杀风险分类方面显示出令人鼓舞的结果。它也易于应用于临床实践，并有助于与患者进行实时沟通，从而能够设计出更好的干预策略。
