Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records-based mortality prediction

DOI10.1002/SIM.9642zbMATH Open1548.62491MaRDI QIDQ6629964

Hulin Wu, Duo Yu

Publication date: 30 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

predictive modelingvariable importanceelectronic health recordsinterpretable machine learning

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

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• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Greedy function approximation: A gradient boosting machine.
• An efficient explanation of individual classifications using game theory
• How to explain individual classification decisions
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• Definitions, methods, and applications in interpretable machine learning
• Random forests

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