Background Cardiotoxicity is a serious adverse event associated with some of the most effective breast cancer therapies. Currently, it is difficult to predict which patients will develop cardiotoxicity due to the multiplicity of clinical, behavioral, and biological factors involved. MethodsHere we describe an effort to apply biomedical informatics approaches to patient data from MedStar Health’s EHR systems to discover and characterize factors that contribute to cardiotoxicity in a real world breast cancer population.ResultsData wrangling techniques including merging data from disparate clinical systems, data transformation, and de-identification of personal health information (PHI)were appliedto the raw clinical data to produce a structured integrated dataset for predictive analysis and hypothesis generation. Using this dataset as input, weshowed howpredictive models can be developed to identify patients at high risk for cardiotoxicity. ConclusionsWe demonstrate how suchmodels can be used for hypothesis generation and data exploration with the ultimate goal of developing applications for precision medicine.
