使用机器学习模型预测一般人群中患有下值失眠的自杀念头和抑郁症
Polona Rus Prelog1,2, Teodora Matić3, Peter Pregelj4,5
1Centre for Clinical Psychiatry, University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia. polona.rus@psih-klinika.si.
BMC psychiatry
|October 17, 2025
概括
机器学习模型有效地使用间接问题选自杀念头和抑郁症,即使在失眠的个体中也是如此. 这种方法为高风险人群提供了早期发现和干预的机会.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 失眠是抑郁症和自杀倾向的重要危险因素.
- 这些情况往往在睡眠投诉的个体中没有被发现.
- 机器学习 (ML) 模型是为了间接选自杀念头 (SI) 和抑郁症而开发的.
研究的目的:
- 开发和验证用于SI和抑郁症间接查的ML模型.
- 评估具有或没有失眠症状的个体的模型性能.
主要方法:
- 后勤回归模型使用间接预测因素 (社会人口统计学,生活满意度,应对策略) 来预测SI (SIDAS) 和抑郁症 (DASS-21).
- 分析了斯洛文尼亚全国性样本 (N=2,989) 的数据.
- 模型性能在具有 (n=917) 和没有 (n=819) 失眠症状 (ISI评分≥8) 的样本上得到验证.
主要成果:
- 机器学习模型显示了跨组的强大和一致的预测性能.
- 对SI的接收器操作特征曲线 (AUROC) 下的面积为0.78 (失眠) 和0.80 (非失眠).
- 抑郁症的AUROC为0.79 (失眠) 和0.82 (非失眠),表明强大且同等有效.
结论:
- 使用间接问题的ML模型有效地同时选自杀倾向和抑郁症.
- 模型在失眠患者中表现强,提供可行,道德和高效的早期检测工具.
- 这种方法使得高风险人群的及时干预成为可能,可能减少自杀和与抑郁症相关的死亡率.
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