保护隐私的联合模型预测抑郁症患者的双极过渡:预测模型开发研究
Dong Yun Lee1, Byungjin Choi1, Chungsoo Kim2
1Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si, Republic of Korea.
Journal of medical Internet research
|July 20, 2023
概括
一个联合学习模型准确地预测了使用多机构数据从抑郁症到双相情感障碍的过渡. 这种方法提高了与本地模型相比的预测准确性,改善了心理健康诊断.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 医疗保健中的机器学习
- 公共卫生 公共卫生
背景情况:
- 情绪障碍,特别是双相情绪障碍,对公共卫生构成重大挑战.
- 早期预测抑郁症转化为双相情感障碍至关重要,但由于症状重叠而难以预测.
- 联合学习为开发预测模型提供了一种新的方法,而无需共享敏感患者数据.
研究的目的:
- 开发和验证一个联合的,差异化的私人,多机构模型,用于预测双相情感障碍过渡.
- 与本地开发的模型相比,评估联合模型的性能.
主要方法:
- 一项回顾性研究,涉及来自韩国4家三级医院的17631名患者,用于模型开发.
- 标准化管道被用来提取跨机构的临床特征.
- 针对特征选择和模型培训,应用了带有差异隐私的联合学习,并对第五家医院的4541名患者进行了外部验证.
主要成果:
- 与本地模型 (AUC 0.642-0.707内部,0.642-0.699外部) 相比,联合模型实现了更高的性能 (内部AUC 0.726,外部AUC 0.719).
- 双极转变的关键预测因素包括查尔森指数,严重抑郁症,抗焦虑药物,年轻年龄和季节性 (春季/夏季).
结论:
- 通过使用分布式精神病学数据,成功开发和验证了一种差异化私有联合模型.
- 联合模型的表现优于仅在本地数据上训练的模型,突出了其在现实世界临床环境中的潜力.
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