arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.
By Hsiao-Ying Lu, Kwan-Liu Ma
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
By Anirudh Rayas, Yuan Wang, Pavan Turaga
arXiv:2608.24688v1 Announce Type: new
Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
By Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt, Adam Casson, Ludmila Tydlitatova, Michal Zelechowski, Ezra E. W. Cohen, Jyoti D. Patel, Max Banaszak, Caitlin McWilliams, Shane Colley, Kate Sasser, Ryan Fukushima, Eric Lefkofsky, Razik Yousfi, Siqi Liu
arXiv:2606. 02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences.
By Sihang Zeng, Matthew Thompson, Ruth Etzioni, Meliha Yetisgen
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.
By Navid Seidi, Satyaki Roy, Sajal K. Das
Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation in...