选择或调整? 选择或调整? 早期治疗阶段的信息如何提高在基于互联网的抑郁症治疗中没有反应的预测
Leona Hammelrath1, Kevin Hilbert2, Manuel Heinrich1
1Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Psychological medicine
|December 13, 2023
概括
机器学习模型可以预测抑郁症治疗结果. 使用早期治疗数据,而不仅仅是基线信息,显著提高了对在线治疗不响应的预测准确性.
科学领域:
- 数字健康数字健康
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
背景情况:
- 基于互联网的干预措施对抑郁症的有效性与面对面治疗相美.
- 低的患者应答率 (超过50%) 仍然是抑郁症治疗的挑战.
- 机器学习 (ML) 通过预测个体结果,为个性化治疗提供了潜力.
研究的目的:
- 为了比较ML算法来预测对6周的在线抑郁症计划没有反应.
- 评估基线与早期治疗特征对预测结果的有用性.
- 在基于互联网的抑郁症治疗中确定治疗成功的关键预测因素.
主要方法:
- 在1270名参与者的自我报告和过程特征 (基线和2周数据) 上训练随机森林算法.
- 基于缺乏可靠和临床上显著的PHQ-9治疗后变化而定义的非响应者.
- 将ML模型与使用80/20列车测试的物流回归基准进行比较,分为100次代.
主要成果:
- 结合早期治疗特征的模型取得了最佳表现 (回忆:0.75-0.76;AUC:0.71-0.77).
- 治疗联盟和早期减轻症状是最重要的预测因素.
- 仅使用基线数据的模型没有超过基准模型的表现.
结论:
- 准确预测不响应需要结合早期治疗阶段的数据.
- 在治疗期间调整治疗,使用随时可用的自我报告数据,似乎比先验患者选择更可行.
- 进一步的实施试验是必要的,以确认临床效用.
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