160 citations · 267 across the 9 of their papers we have counts for
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cs.LG2022
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
Seyed Ehsan Saffari, Yilin Ning, Xie Feng +5
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 fo…
cs.LG2022★ 32 cited
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
Yilin Ning, Siqi Li, Marcus Eng Hock Ong +4
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 import…