39 citations · 49 across the 8 of their papers we have counts for
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stat.ML2014★ 2 cited
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock +5
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 ri…
stat.ML2014★ 4 cited
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Julian Wolfson, Sunayan Bandyopadhyay, Mohamed Elidrisi +5
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…