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. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.
The study introduces a graph‑enhanced framework for discovering disease trajectories and forecasting temporal endpoints using longitudinal EHR data from MIMIC‑IV v3.1. It constructs patient‑specific motifs from consecutive admissions, applies a time‑aware Transformer to select up to 24 motifs per endpoint, and reports modest improvements in micro‑AUPRC and event‑time MAE, with larger gains for events occurring after 730 days. The authors also explore branching‑motif conditioning and GNN‑based edge repair, finding that motifs mainly provide long‑range timing support rather than superior endpoint discrimination or survival prediction.
arXiv:2607. 19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks.
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.
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
The paper introduces a compact patient world model that forecasts digital health campaign outcomes by maintaining a latent state per patient and learning exposure‑conditioned dynamics. Evaluated on a large US campaign dataset, the model predicts new‑to‑brand prescription volume with low relative error (2.9% at week‑4 cutoff) compared to much higher errors from baseline classifiers. The study also shows that dense next‑exposure supervision is crucial for accurate forecasts when conversions are rare and highlights limitations in interpreting exposure‑conditioned rollouts causally.
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...
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
arXiv:2610.17059v1 Announce Type: new Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing m...
FedDRAW introduces a new server‑side aggregation technique for federated learning called Federated Dual Reputation Annealing Weighting. It blends a data‑size prior with cosine similarity between client and global model parameters, using two annealing schedules to shift weight from size to similarity and to gradually relax selectivity. Experiments on 12 simulated client partitions of CheXpert and ChestMNIST datasets show FedDRAW outperforms seven federated baselines in AUC and geometric mean of sensitivity and specificity, with statistically significant results.