预测乌干达艾滋病毒感染者的自杀倾向:一种机器学习方法
Anthony B Mutema1,2,3, Lillian Linda1,3, Daudi Jjingo1
1African Center of Excellence in Bioinformatics and Data Intensive Science, Makerere University, Kampala, Uganda.
Frontiers in psychiatry
|September 2, 2025
概括
机器学习模型可以预测艾滋病毒感染者的自杀风险. 纳入遗传数据 (多基因风险评分) 提高了模型的敏感性,有助于早期识别和干预风险人群.
科学领域:
- 计算精神病学
- 艾滋病的流行病学
- 心理健康的遗传学
背景情况:
- 与一般人群相比,感染艾滋病毒 (PLWH) 的人有更高的自杀想法和行为风险.
- 目前用于预测PLWH中自杀倾向的方法是不够的.
- 机器学习 (ML) 提供了一个有前途的方法来模拟导致自杀行为的复杂因素.
研究的目的:
- 评估ML在乌干达PLWH中预测自杀倾向的潜力.
- 评估将自杀性多基因风险评分 (PRS) 纳入ML模型的影响.
- 确定这一群体中自杀倾向的关键预测因素.
主要方法:
- 一个回顾性病例控制研究,使用了乌干达1,126个PLWH的纵向数据.
- 开发和评估八个ML算法,包括成本敏感的AdaBoost.
- 在282名参与者中计算自杀性PRS,并将其纳入ML模型.
- 性能指标包括AUC,PPV,灵敏度,特异性和MCC.
主要成果:
- 对于预测基线自杀倾向,成本敏感的AdaBoost的AUC为0. 79.
- 该模型显示了普遍性,AUC分别为12个月流行和发生自杀的0. 75.
- 使用PRS提高了敏感度6%,但降低了特异性9%.
- 主要抑郁症诊断和高压力水平是重要的预测因素.
结论:
- 一个成本敏感的AdaBoost模型可以预测乌干达的PLWH的自杀风险,尽管具有适度的积极预测值.
- 整合自杀性PRS可以提高自杀性风险的ML模型的预测性能.
- 需要对不同群体进行进一步的研究,以验证PRS的实用性和临床适用性.
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