A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes

A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes

Summary: The AutoScore framework can automatically generate data-driven clinical scores in various clinical applications. Here, we present a protocol for developing clinical scoring systems for binary, survival, and ordinal outcomes using the open-source AutoScore package. We describe steps for pack...

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Bibliographic Details
Main Authors: Feng Xie, Yilin Ning, Mingxuan Liu, Siqi Li, Seyed Ehsan Saffari, Han Yuan, Victor Volovici, Daniel Shu Wei Ting, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Roger Vaughan, Bibhas Chakraborty, Nan Liu
Format: Article
Language:English
Published: Elsevier 2023-06-01
Series:STAR Protocols
Subjects:
Health Sciences
Computer sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S2666166723002691

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http://www.sciencedirect.com/science/article/pii/S2666166723002691

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