Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records

We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically relevant high-recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices. MIs are common and deadly, causing one in six deaths overall in the United States totaling 400,000 per year (Roger et al. 2011). Because of its medical significance, MI has been studied in depth, mostly in the fields of epidemiology and biostatistics, yet rarely in machine learning.