Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes

Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes

Abstract Background Building prognostic models of clinical outcomes is an increasingly important research task and will remain a vital area in genomic medicine. Prognostic models of clinical outcomes are usually built and validated utilizing variable selection methods and machine learning tools. The...

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Bibliographic Details
Main Authors: Lira Pi, Susan Halabi
Format: Article
Language:English
Published: BMC 2018-09-01
Series:Diagnostic and Prognostic Research
Subjects:
Variable selection
Calibration
Overfitting
Machine learning
Proportional hazards model
Prognostic models
Online Access:http://link.springer.com/article/10.1186/s41512-018-0043-4

Internet

http://link.springer.com/article/10.1186/s41512-018-0043-4

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