Brisimi, Theodora S.Xu, TingtingWang, TaiyaoDai, WuyangAdams, William G.Paschalidis, Ioannis Ch.2018-06-202018-06-202018-04Theodora S. Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, William G. Adams, Ioannis Ch. Paschalidis. 2018. "Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach." Proceedings of the IEEE, v. 106, issue 4, pp. 690 - 707.0018-92191558-2256https://hdl.handle.net/2144/29593Urban 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.p. 690 - 707Artificial intelligence and image processingBiomedical engineeringElectrical and electronic engineeringDiseasesMedical servicesDiabetesPredictive modelsSmart citiesHospitalsClustering methodsElectronic healthcareMachine learningArticle10.1109/JPROC.2017.27893190000-0002-3343-2913 (Paschalidis, Ioannis Ch.)