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Across all settings, RSF models trained on approximately 25 − 40% of each risk set achieve concordance index value that are very similar from those obtained using full risk sets, while reducing training time by more than half.
This framework introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases.
The method proposed leverages invertible residual neural networks (i-ResNets) to equip generalized linear models with both nonlinear parameter estimation and a flexible correction of their distributional assumptions while always retaining stochastic monotonicity of the modeled distribution in the (formerly linear) pred...
xMICD provides a practical way to integrate embedding-based semantic relationships into interpretable clinical feature spaces for machine learning models, and achieves predictive performance comparable to embedding-based representations such as ICD2Vec across multiple clinical prediction tasks.
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