基于智能手机的自杀预防平台中的不规则和不完整的时间系列问卷预测自杀念头:试点研究
Gwenolé Quellec1, Sofian Berrouiguet2,3, Margot Morgiève4,5,6,7
1Inserm, UMR 1101, LaTIM, IBRBS building, 22 avenue Camille Desmoulins, 29200, Brest, France. gwenole.quellec@inserm.fr.
Scientific reports
|September 6, 2024
概括
这项研究开发了一个使用智能手机数据预测自杀风险的AI模型. 人工智能准确地识别了高自杀念头 (SI),并预测了其进展,为早期干预提供了一个新的工具.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能
- 心理健康研究 心理健康研究
背景情况:
- 自杀每年造成70万多人死亡,需要改进风险评估方法.
- 纵向,现实世界的数据收集对于理解自杀风险的时间动态至关重要.
- 现有的方法往往缺乏及时干预所需的细节性.
研究的目的:
- 使用智能手机收集的数据开发和验证一个AI模型来评估和预测自杀念头 (SI).
- 调查SI在自杀风险较高的人群中的时间进展.
- 确定与自杀风险波动相关的关键心理和社会因素.
主要方法:
- 通过EMMA智能手机应用程序收集了89名处于风险人群的纵向自我评估数据.
- 开发了一个人工智能模型,其中包含一个缺失的值归算算法,以处理不均的间隔,不完整的时间序列数据.
- 训练AI评估当前的SI水平并预测未来的SI进展.
主要成果:
- 人工智能模型在当天 (AUC=0.804) 和提前三天 (AUC=0.769) 区分高和低SI水平方面表现出显著的准确性.
- 关键预测因素包括过去的SI,心理疼痛,福祉,激动,情绪紧张以及社会接触和休活动等保护因素.
- 该模型有效地管理了缺失的数据,这是现实世界的纵向研究中常见的挑战.
结论:
- 智能手机收集数据的AI驱动分析显示,对早期自杀风险预测有前途.
- 艾玛应用程序和人工智能模型提供了一个潜在的工具,用于持续监测和及时干预处于风险中的个人.
- 了解心理和社会因素对于改进AI驱动的自杀风险评估至关重要.
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