Discriminative boosted Bayes networks for learning multiple cardiovascular procedures

We consider the problem of predicting three procedures, viz, EKG, Angioplasty and Valve Replacement procedures jointly from Electronic Health Records (EHR) and develop a discriminative boosted Bayesian network algorithm. Differences between our proposed approach and standard Bayes Net structure learners are (1) we do not assume that the number of features (observations) are uniform across training examples and (2) our method explicitly handles the precision-recall tradeoff. Our empirical evaluations on a real EHR data demonstrates the superiority of this proposed approach to learning these procedures individually.