most citedA Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

4 citations · 6 across the 2 of their papers we have counts for

collaborators

2 papers

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…