多层次无限因子模型对老年患者手术并发症的预测
Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients
摘要：
我们开发了一个分层无限潜在因子模型（HIFM），以适当地考虑数据中各亚群之间的协方差结构。我们提出了一种新的分层狄利克雷过程收缩之前的负载矩阵，灵活地捕捉我们的数据的底层结构，跨亚群，同时共享信息，以提高推理和预测。先验的破棒构造假设了无限数量的因子，并允许每个亚群利用因子空间的不同子集，并选择最佳解释变异所需的因子数量。理论结果提供了支持的先验。我们将此模型发展成一种潜在因子回归方法，它擅长于回归系数的预测和推断。仿真被用来验证这种强大的性能相比，基线方法。我们将这项工作应用于杜克大学卫生系统（DUHS）老年患者使用电子健康记录数据预测手术并发症的问题。我们利用DUHS的额外手术机会来加强目标患者的学习。使用HIFM来识别高风险患者将预测死亡的灵敏度从基于当前使用的启发式的35%提高到91%。
英文摘要：
We develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data across subpopulations while sharing information to improve inference and prediction. The stick-breaking construction of the prior assumes infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain the variation. Theoretical results are provided to show support of the prior. We develop the model into a latent factor regression method that excels at prediction and inference of regression coefficients. Simulations are used to validate this strong performance compared to baseline methods. We apply this work to the problem of predicting surgical complications using electronic health record data for geriatric patients at Duke University Health System (DUHS). We utilize additional surgical encounters at DUHS to enhance learning for the targeted patients. Using HIFM to identify high risk patients improves the sensitivity of predicting death to 91% from 35% based on the currently used heuristic.
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