An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
SurvSHAP(t): Time-dependent explanations of machine learning survival models
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Deep survival models for Alzheimer's progression are robust but exhibit considerable bias across sensitive attributes, which the authors quantify using two newly proposed fairness metrics.
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Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
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Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
Deep survival models for Alzheimer's progression are robust but exhibit considerable bias across sensitive attributes, which the authors quantify using two newly proposed fairness metrics.