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Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Our techniques are motivated by and illustrated on data from a large U. S.
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts.
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