arXiv:2601. 14609v2 Announce Type: replace-cross Abstract: Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environments block patient-level data sharing and the persistent server connections required by iterative federated methods.
arXiv:2606. 23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data.
arXiv:2608. 02939v1 Announce Type: new Abstract: Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer.
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.
arXiv:2510. 04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure.
arXiv:2509. 22352v3 Announce Type: replace Abstract: Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death.
arXiv:2606. 20115v1 Announce Type: new Abstract: Conformal risk control (CRC) provides distribution-free guarantees on segmentation quality by calibrating a prediction-set threshold on held-out data.
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.
The paper introduces Scaling Electronic Health Record Foundation Models for Population Health Management, a large‑scale model trained on billions of medical events from over 5 million patients in Taiwan and the United States. By aligning ICD codes across different health systems, the model achieves strong scaling and generalization across 11 chronic disease prediction tasks, outperforming tree‑based, general, and biomedical language models with high sensitivity at 99% specificity. It also demonstrates superior few‑shot performance on the EHRShot benchmark and shows that cross‑system alignment provides a stronger pretraining signal than single‑site duplication in data‑limited scenarios.
arXiv:2512.16481v2 Announce Type: replace-cross Abstract: Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survi...
Adaptive Bayesian Partner Selection (ABPS) is a peer‑to‑peer federated learning framework designed for heterogeneous clinical centers, where each center maintains a Beta‑Bernoulli posterior over prospective peers’ Shapley marginal utility and selects partners using an Upper Confidence Bound criterion. The lightweight propose‑reject mechanism allows centers to collaborate only when mutually beneficial, with the option to abstain from communication entirely. In experiments on 230 non‑IID ICU centers predicting in‑hospital mortality, the ABPS‑X variant achieves comparable accuracy to the strongest baseline (FedDyn) while reducing communication cost by 90% and enabling intentional isolation for many centers.
arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modelin...