AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes

AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes

Abstract Background Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learning–based automatic clinical score generator for binary outcomes. This s...

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Bibliographic Details
Main Authors: Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
Format: Article
Language:English
Published: BMC 2022-11-01
Series:BMC Medical Research Methodology
Subjects:
Interpretable machine learning
Medical decision making
Clinical score
Ordinal outcome
Electronic health records
Online Access:https://doi.org/10.1186/s12874-022-01770-y

Internet

https://doi.org/10.1186/s12874-022-01770-y

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