Diniz Evandro J S, Fontenele José E, de Oliveira Adonias C, Bastos Victor H, Teixeira Silmar, Rabêlo Ricardo L, Calçada Dario B, Dos Santos Renato M, de Oliveira Ana K, Teles Ariel S
Federal Institute of Maranhão, Araioses 65570-000, Brazil.
Technological Neuro Innovation Laboratory, Federal University of Delta do Parnaíba, Parnaíba 64202-020, Brazil.
Healthcare (Basel). 2022 Apr 8;10(4):698. doi: 10.3390/healthcare10040698.
People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the tool, a solution that collects textual data from users' smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.
有自杀风险的人往往比较孤立，无法分享他们的想法。因此，自杀意念监测成为一项艰巨的任务。所以，需要以一种能够识别有自杀风险的人是否以及何时产生自杀意念的方式对他们进行监测，以便专业人员能够及时进行干预。本研究旨在开发一种工具，该工具可以从用户的智能手机收集文本数据，并识别自杀意念的存在。该解决方案有一个虚拟键盘移动应用程序，它可以被动收集用户文本并将其发送到一个网络平台进行处理。该平台使用自然语言处理和深度学习模型对文本进行分类，以识别自杀意念，并将结果在仪表板上呈现给心理健康专业人员。使用不同的机器学习/深度学习算法进行情感分析的文本分类。进行了一项验证研究，以确定性能结果最佳的模型。BERTimbau Large模型表现更好，召回率达到0.953（准确率：0.955；精确率：0.961；F值：0.954；曲线下面积：0.954）。所提出的工具展示了从用户文本中识别自杀意念的能力，这使其能够在与专业人员及其患者的研究中进行试验。
