A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

Abstract Background Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a lik...

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Bibliographic Details
Main Authors: Brian Connolly, K. Bretonnel Cohen, Daniel Santel, Ulya Bayram, John Pestian
Format: Article
Language:English
Published: BMC 2017-08-01
Series:BMC Bioinformatics
Subjects:
Statistics
Nonparametric
Bayesian
Calibration
Machine learning
Online Access:http://link.springer.com/article/10.1186/s12859-017-1736-3

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http://link.springer.com/article/10.1186/s12859-017-1736-3

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