被动检测行为变化预防自杀企图
Passive detection of behavioral shifts for suicide attempt prevention
摘要：
全世界每年有超过一百万人自杀。日常护理费用、社会耻辱和治疗问题仍然是心理健康领域需要克服的困难。精神障碍的大多数症状与患者的行为状态有关，例如活动或社会活动。基于移动的技术允许被动收集患者数据，这补充了依赖于有偏见的问卷和偶尔的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的机器学习（ML）模型来检测精神病患者的行为转变，从智能手机应用程序收集的不引人注目的数据。我们的临床验证结果揭示了早期检测移动工具的想法，用于预防自杀企图的任务。
英文摘要：
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.
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