Random jump forests estimate multi-state transition rates without Markov assumption
One tree partitions covariates for all transitions, with Nelson–Aalen estimates and an R package.
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Relatedbiomedical1competing-risks1deep-learning1medical-ai1multi-state-models1random-forests1r-package1tabular1
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Stories tagged #survival-analysis
The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks
A 23-dataset benchmark finds cumulative-incidence loss, not backbone choice, drives competing-risks performance.
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
GeneICL, a 4.2M-parameter tabular model, leads 80 transcriptomic outcome tasks without inference-time training.
DynSHAP: Towards Explainable Dynamic Survival Analysis
DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.