对抗治疗抑郁症风险模型的概括性在三个健康系统中
Colin G Walsh1,2,3,4, Michael Ripperger1, Thomas H McCoy5
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
medRxiv : the preprint server for health sciences
|June 10, 2025
概括
早期识别患有耐治疗抑郁症的个体至关重要. 使用电子健康记录的机器学习模型在卫生系统中显示出有限的预测准确性,突出了需要改进数据的需要.
科学领域:
- 临床信息学 临床信息学
- 精神病学是一个精神病学.
- 医疗保健中的机器学习
背景情况:
- 治疗耐药性重度抑郁症 (TRD) 的有效管理需要及早识别有风险的个体.
- 目前用于TRD管理的策略正在演变,这强调了对预测工具的需求.
- 早期识别可能患有TRD的患者对于及时干预至关重要.
研究的目的:
- 开发和验证机器学习模型,以使用电子健康记录 (EHR) 预测耐治疗抑郁症.
- 评估各种医疗保健系统中预测模型的通用性和性能.
- 识别影响模型性能和不一致性的关键特征.
主要方法:
- 从三个不同的健康系统 (MGB,VUMC,GC) 提取了EHR数据,用于患有严重抑郁症 (MDD) 的患者.
- 根据特定的治疗标准 (例如,ECT,胺,多次失败试验) 定义的耐治疗抑郁症 (TRD).
- 应用L1-规范化回归和随机森林分析对社会人口统计,药物和诊断代码数据.
主要成果:
- 模型的歧视性能各不相同,在外部验证中,ROC曲线下的面积 (AUROCs) 从0.51-0.58不等.
- 模型之间的一致性相关系数 (CCC) 较低 (0.13-0.38),表明卫生系统之间的协议有限.
- 年龄和性别是模型不一致的主要预测因素,表明潜在的偏见.
结论:
- 线性模型显示,在整个卫生系统中,总体表现一致但个体表现不一致.
- 提高预测准确性可能需要结合超出标准EHR信息的数据类型.
- 需要进行进一步的研究,以确保各子组的公平表现,并评估早期识别的临床效用.
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