A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study

Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying important predictors to create parsimonious scores, but such ‘black box’ variable selection limits interpretability, and varia...

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Bibliographic Details
Main Authors:	Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2022-06-01
Series:	PLOS Digital Health
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931273/?tool=EBI

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort stu... by Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu

Published 2022-06-01

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A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study

A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort stu... by Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu

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