Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

no code implementations • 3 Jan 2018 • Theodora S. Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, William G. Adams, Ioannis Ch. Paschalidis

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.

Binary Classification Clustering +1