← Back to all news
arXiv Statistics ML By Hemant Ishwaran, Eileen M. Hsich, Udaya B. Kogalur, Donald K. K. Lee

Random Hazard Forests

Read the original on arXiv Statistics ML →

Random Hazard Forests (RHF) is a survival tree ensemble that models how a patient's hazard changes over continuous time as new measurements arrive. RHF directly estimates a nonparametric hazard likelihood for predictable covariate processes, using an efficient working model to guide tree construction and then estimating flexible time‑varying hazards at each terminal node. By routing each tree based on the covariate state immediately before each time point, RHF can handle irregular and asynchronous covariate updates, and averaging across trees yields a pathwise hazard estimate that accurately captures changing risk in simulations and an intensive‑care application.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

Related stories

arXiv AI
3d ago

PGP-Clinical-TimeKAN: Prior-Guided Joint Probabilistic Forecasting of Clinical Trajectories

PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate physiological data, combining missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov‑Arnold messages, and a low‑rank multivariate Student‑t head. Evaluated on a MIMIC‑IV cohort of 6,882 patients, it achieves the second‑lowest normalized MAE and the lowest RMSE among 13 models, while providing calibrated probabilistic forecasts with empirical coverage at 50%, 80%, and 95% intervals. Ablation studies show that relational structure is critical for performance, and increasing covariance rank improves likelihood but not point accuracy. whyItMatters":"The model demonstrates that joint trajectory forecasting can yield highly accurate, calibrated predictions of physiological trajectories, offering a potentially inspectable intermediate task for clinical deterioration prediction."

By Weizhi Nie, Rihao Chang, Weijie Wang, Yuting Su
benchmarks
More like this →