在社区实践环境中预测抗抑郁药治疗的结果
Gregory E Simon1, Maricela Cruz1, Jennifer M Boggs1
1Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck).
Psychiatric services (Washington, D.C.)
|December 5, 2023
概括
分析电子健康记录 (EHR) 的机器学习模型无法准确预测患者对抗抑郁药物的反应. 个性化抑郁症治疗需要对早期结果进行系统评估,而不仅仅是EHR数据.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 抗抑郁药治疗的反应在个体之间有很大差异.
- 电子健康记录 (EHR) 包含丰富的数据,可以预测治疗结果.
- 准确预测抗抑郁药反应对于个性化医学至关重要.
研究的目的:
- 利用EHR数据评估机器学习模型在预测患者对抗抑郁药物的反应方面的有效性.
- 确定EHR衍生的特征是否可以识别可能从特定抗抑郁药中受益的患者.
主要方法:
- 利用了2469名开始抗抑郁药治疗的患者的EHR数据.
- 提取了人口统计,诊断,药物和PHQ-9分数数据作为预测因素.
- 采用随机森林和处罚回归模型来预测治疗反应和随访得分.
主要成果:
- 模型在预测和观察后的随访得分之间实现了0.38的相关性.
- 接受器操作特征曲线下的面积,有利响应为0.57.
- 预测准确性不足以指导关于抗抑郁药开始的临床决策.
结论:
- 使用当前EHR数据的机器学习模型对预测抗抑郁药物治疗反应不够准确.
- 对抑郁症治疗的临床决策应优先考虑对早期患者结果的系统监测.
- 未来的研究可能会探索更复杂的方法或额外的数据源,以改善预测.
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