Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
This addresses the problem of managing chronic disease risks for patients and healthcare providers in urban settings, though it is incremental in applying existing ML techniques with new interpretability methods.
The paper tackled predicting hospitalizations for heart disease and diabetes from Electronic Health Records using machine learning, achieving improved accuracy with novel interpretable methods like K-LRT and Joint Clustering and Classification validated on large datasets from Boston Medical Center.
Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England.