Title: A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure,
Abstract: Electronic medical records (EMR) contain longitudinal information about
patients that can be used to analyze outcomes. Typically, studies on EMR data
have worked with established variables that have already been acknowledged to
be associated with certain outcomes. However, EMR data may also contain
hitherto unrecognized factors for risk association and prediction of outcomes
for a disease. In this paper, we present a scalable data-driven framework to
analyze EMR data corpus in a disease agnostic way that systematically uncovers
important factors influencing outcomes in patients, as supported by data and
without expert guidance. We validate the importance of such factors by using
the framework to predict for the relevant outcomes. Specifically, we analyze
EMR data covering approximately 47 million unique patients to characterize
renal failure (RF) among type 2 diabetic (T2DM) patients. We propose a
specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to
identify the important factors from those available from patient encounter
history. To validate the identified factors, we use a specialized generalized
linear model (GLM) to predict the probability of renal failure for individual
patients within a specified time window. Our experiments indicate that the
factors identified via our data-driven method overlap with the patient
characteristics recognized by experts. Our approach allows for scalable,
repeatable and efficient utilization of data available in EMRs, confirms prior
medical knowledge and can generate new hypothesis without expert supervision.