  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.
