We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009--2011 inpatient episodes, and then tested the model on 2012 episodes. The model scored an AUROC of approximately 0.76 on predicting all-cause readmissions -- defined using official Centers for Medicare and Medicaid Services (CMS) methodology -- or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, we provide what blackboxes cannot -- the exact gold-standard global interpretation of the model, identifying relative risk factors and quantifying the effect of discharge placement. We also show that the posthoc explainer SHAP fails to provide accurate explanations.

翻译：我们开发了一种固有可解释的多层次贝叶斯框架，用于模拟回归系数的变异，该框架模仿了ReLU激活深度神经网络的逐段线性特性。利用该框架，我们构建了一个基于医疗理赔预测再入院和死亡的生存模型，重点关注出院安置因素，并在估计因果局部平均处理效应时调整了混杂因素。我们基于2009-2011年住院病例，使用2008年和2011年5%的Medicare受益人样本训练模型，并在2012年病例上进行了测试。该模型预测出院后30天内全因再入院（采用美国医疗保险和医疗补助服务中心官方定义）或死亡的AUROC约为0.76，性能与XGBoost和贝叶斯深度神经网络相当，表明精确性与可解释性并非不可兼得。关键在于，作为回归模型，我们提供了黑箱模型无法实现的精确黄金标准全局解释——识别相对风险因素并量化出院安置的影响。我们还证明，事后解释工具SHAP无法提供准确的解释。