可解释的 (不仅仅是事后可解释的) 医疗索赔建模用于出院安排,以减少可预防的所有原因再入院或死亡
Ted L Chang1,2, Hongjing Xia1,2, Sonya Mahajan1,2
1Sound Prediction Inc., Columbus, OH, United States of America.
PloS one
|May 9, 2024
概括
我们创建了一个可解释的贝叶斯生存模型,用医疗索赔来预测医院再入院和死亡. 这种可解释的模型实现了高精度,与复杂的方法竞争,同时提供了对风险因素的清晰洞察力.
科学领域:
- 机器学习 机器学习
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 预测再入院和死亡对于改善医疗保健质量至关重要.
- 现有的预测模型往往缺乏解释性,阻碍了信任和临床采用.
- 深度神经网络提供了高准确度,但作为"黑子"的功能,限制了对其预测的理解.
研究的目的:
- 为生存分析开发一个内在可解释的多层次贝叶斯框架.
- 使用医疗索赔数据创建医院再入院和死亡的预测模型.
- 为了证明可解释性和准确性在预测建模中不是相互排斥的.
主要方法:
- 开发了一个多层贝叶斯框架,模仿ReLU激活的深度神经网络,用于回归系数变化.
- 制定了一个生存模型,重点关注放电位置,调整混以估计因果关系.
- 在大量医疗保险受益者的住院病例样本上训练并测试了该模型.
主要成果:
- 在预测30天全因再入院或死亡时,获得了大约0.75的非样本AUROC.
- 该模型表现出与XGBoost和贝叶斯深度神经网络相比具有竞争力的性能.
- 确定了相对风险因素,并量化了排放放置的影响,提供了整体解释.
结论:
- 开发的贝叶斯框架在生存预测中提供了高准确性和固有的解释性.
- 该模型为预测提供了明确的推理,与黑盒模型不同.
- 像SHAP这样的后期解释者可能会提供与基本真相模型推理不一致的解释.
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