  Models for predicting the risk of cardiovascular events based on individual
patient characteristics are important tools for managing patient care. Most
current and commonly used risk prediction models have been built from carefully
selected epidemiological cohorts. However, the homogeneity and limited size of
such cohorts restricts the predictive power and generalizability of these risk
models to other populations. Electronic health data (EHD) from large health
care systems provide access to data on large, heterogeneous, and
contemporaneous patient populations. The unique features and challenges of EHD,
including missing risk factor information, non-linear relationships between
risk factors and cardiovascular event outcomes, and differing effects from
different patient subgroups, demand novel machine learning approaches to risk
model development. In this paper, we present a machine learning approach based
on Bayesian networks trained on EHD to predict the probability of having a
cardiovascular event within five years. In such data, event status may be
unknown for some individuals as the event time is right-censored due to
disenrollment and incomplete follow-up. Since many traditional data mining
methods are not well-suited for such data, we describe how to modify both
modelling and assessment techniques to account for censored observation times.
We show that our approach can lead to better predictive performance than the
Cox proportional hazards model (i.e., a regression-based approach commonly used
for censored, time-to-event data) or a Bayesian network with {\em{ad hoc}}
approaches to right-censoring. Our techniques are motivated by and illustrated
on data from a large U.S. Midwestern health care system.
