在精神病急诊室对自杀想法和行为进行个性化机器学习决策支持
Franco Gericke1, Wouter Voorspoels2, Elke Peeters3
1Department of Psychiatry, Centre for Public Health Psychiatry, KU Leuven, Leuven, Belgium; Institute for Life Course Health Research, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa.
Psychiatry research
|August 21, 2025
概括
机器学习模型使用精神病急诊室的EHR数据可以预测短期的自杀风险. 解释性人工智能可以增强针对性干预的模型, 改善患者的护理.
科学领域:
- 精神病学与人工智能
- 临床决策支持系统
- 公共卫生和预测分析
背景情况:
- 在心理急诊室的患者面临着高短期自杀想法和行为 (STB) 的风险.
- 目前用于识别高风险个体的工具不足,阻碍了有效的临床决策.
- 需要改进,可解释和个性化的STB预测方法.
研究的目的:
- 使用XGBoost算法预测1,6和12个月内自杀想法 (SI) 和自杀企图 (SA) 的风险.
- 通过可解释的人工智能 (XAI) 方法提高这些预测模型的可解释性.
- 评估人工智能驱动的心理急诊风险预测的临床实用性和成本效益.
主要方法:
- 使用结构化电子健康记录 (EHR) 开发和评估机器学习 (ML) 模型.
- 数据来源于26,198名患者和51,397名精神病急诊室 (PED) 在20年内.
- 用XGBoost算法和本地XAI技术进行模型解释的预测研究设计.
主要成果:
- 模型表现出良好的分辨能力 (AUC 0. 70- 0. 80) 和良好的校准 (ICI 0. 001- 0. 004).
- 预测最高风险的前20%占所有性病转诊病例的50-70%.
- XAI 方法显著提高了预测的透明度和患者层面的可解释性.
- 模型符合成本效益的干预目标标准.
结论:
- 用本地XAI增强的机器学习模型可以有效地识别PED中高风险个体.
- 这些由人工智能驱动的工具可以帮助临床医生实施针对性,具有成本效益的性侵干预措施.
- 改善风险分层有可能减少短期的自杀想法和行为转诊.
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