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.

翻译：每年全世界有100多万人自杀。日常护理、社会污名化和治疗问题的费用仍然是心理健康方面难以克服的障碍。大多数精神失常症状都与病人的行为状态有关,例如流动性或社会活动。移动技术允许被动地收集病人数据,这补充了依赖偏见问卷和偶尔医疗预约的传统评估。在这项工作中,我们提出了一个非侵入性机器学习模式,用以检测精神病患者的行为变化,使其脱离智能电话应用程序收集的不受干扰的数据。我们经临床验证的结果揭示了早期发现预防自杀未遂的移动工具的想法。