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Transcript of JSM 2013
Predicting 5-year risk of cardiovascular events using electronic health record data.
Combines health information from clinic/hospital visits, prescription database, and vital records.
A machine learning technique often used for classification problems.
2. Use assumptions on P(X|Y) to make life easier, e.g., independence = "Naive Bayes":
Electronic health record (EHR)
Censored Bayesian networks
Huge sample sizes
Incomplete follow-up and censoring
Opportunities and challenges
Our dataset provides 10 years of health information on >400,000 people.
BP, cholesterol, etc. measured irregularly (if at all), especially among young people.
CVD is multifactorial and several risk factors may interact.
Extending Naive Bayes to censored, time-to-event data
Naive Bayes assumption &
Julian Wolfson, Assistant Professor, Division of Biostatistics
University of Minnesota School of Public Health
(in collaboration with the UMN/HP iPredict Research Group)
email@example.com z.umn.edu/julianw @DrJWolfson
1. Model class probabilities by applying Bayes' rule:
Yep, this assumption is pretty crazy. Amazingly, Naive Bayes often works pretty well even if the assumption is severely violated!
At the poster session this afternoon:
An app which uses these methods to calculate your five-year cardiovascular risk.