Tree-based regularization for interpretable readmission prediction

Preventable hospital readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery. However, most data driven studies for understanding readmissions have produced non-interpretable black boxes, which precludes them from being used effectively within the decision support systems in the hospitals. A novel strategy to improve the interpretability of a linear model by incorporating domain knowledge is proposed here. The central idea is to exploit the hierarchical relationships among the features (medical diagnosis codes, in this case) using a tree-structured sparsity-inducing regularization norm. The proposed method transforms the hierarchical relations among features into a graph and then applies graph-guided regularization during the model learning. Additionally, an evaluation metric is proposed to quantify the interpretability of a linear model with respect to the domain hierarchy. Results on two healthcare claims data sets are shown, where a model is learnt to predict a patient’s risk of readmission, based on the medical history and other relevant features. Results show that the proposed method is able to learn a model which can predict readmission risk with accuracies that are comparable to existing methods, but produces a highly interpretable output, which allows medical experts to draw clinically relevant insights and identify key factors associated with hospital readmissions. Some of these factors conform to existing beliefs, e.g., impact of surgical complications and infections during hospital stay. Other factors, such as the impact of mental disorder and substance abuse on readmission, provide empirical evidence for several pre-existing but unverified hypotheses. The findings of this study will be instrumental in designing the next generation decision support systems for preventing readmissions.