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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308042
Title: Risk Prediction from Electronic Health Record Data: A Naïve Bayes Approach
Author(s): Julian Wolfson*+
Companies: University of Minnesota
Keywords: risk prediction ; naive bayes ; machine learning ; electronic health record
Abstract:

Predicting an individual's risk of experiencing a clinical outcome (eg. a heart attack) is a statistical task with important consequences for both practicing clinicians and public health experts. One resource for tackling risk prediction problems is electronic health record (EHR) data, which are increasingly available on entire populations under care within U.S. health insurance systems. EHR data are recorded longitudinally, making them ideal for predicting risk, but their dimensionality and complexity challenge standard statistical methods. We present an extension of the traditional Naive Bayes (NB) machine learning approach to risk prediction using EHR data with time-to-event outcomes subject to censoring. We compare its performance to the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application using an EHR dataset from a large Midwest integrated healthcare system.


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