Journal
PROCEEDINGS OF THE IEEE
Volume 106, Issue 4, Pages 690-707Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2017.2789319
Keywords
Diabetes; electronic health records (EHRs); heart disease; machine learning; predictive analytics; smart city; smart health
Categories
Funding
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1237022] Funding Source: National Science Foundation
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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 diseases, 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 (EHRs). 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 (SVMs), 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 data sets from the Boston Medical Center, the largest safety-net hospital system in New England.
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