More than one million people commit suicide every year worldwide. The costs of daily cares, social stigma and treatment issues are still hard barriers to overcome in mental health. Most symptoms of mental disorders are related to the behavioral state of a patient, such as the mobility or social activity. Mobile-based technologies allow the passive collection of patients data, which supplements conventional assessments that rely on biased questionnaires and occasional medical appointments. In this work, we present a non-invasive machine learning (ML) model to detect behavioral shifts in psychiatric patients from unobtrusive data collected by a smartphone app. Our clinically validated results shed light on the idea of an early detection mobile tool for the task of suicide attempt prevention.

中文翻译：

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被动检测行为转变以预防自杀未遂

全世界每年有超过一百万人自杀。日常护理的成本、社会污名和治疗问题仍然是心理健康需要克服的困难障碍。精神障碍的大多数症状与患者的行为状态有关，例如行动不便或社交活动。基于移动的技术允许被动收集患者数据，这对依赖于有偏见的问卷和偶尔的医疗预约的传统评估进行了补充。在这项工作中，我们提出了一种非侵入性机器学习 (ML) 模型，从智能手机应用程序收集的不显眼的数据中检测精神病患者的行为变化。我们的临床验证结果阐明了用于预防自杀未遂任务的早期检测移动工具的想法。