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Cox Regression with Correlation Based Regularization for Electronic Health Records
2013
2013 IEEE 13th International Conference on Data Mining
Survival Regression models play a vital role in analyzing time-to-event data in many practical applications ranging from engineering to economics to healthcare. These models are ideal for prediction in complex data problems where the response is a time-to-event variable. An event is defined as the occurrence of a specific event of interest such as a chronic health condition. Cox regression is one of the most popular survival regression model used in such applications. However, these models have
doi:10.1109/icdm.2013.89
dblp:conf/icdm/VinzamuriR13
fatcat:tyt33zdkvvdhvlhowij6raoxve