The paper introduces a bounded updating framework for ICU prediction models that freezes the physiological encoder while allowing updates only to the treatment pathway and fusion head. Experiments on 84,792 MIMIC-IV ICU stays across four temporal shifts show that selective adaptation yields more stable explanations—higher rank correlation, better top‑5 feature agreement, and improved retrieval stability—compared to full model adaptation. Predictive performance varies by outcome, but the results demonstrate that explanation stability is governed by the structural boundaries of allowed adaptation rather than merely by freezing components.
By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud
arXiv:2605. 10840v3 Announce Type: replace-cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories.
By Yixuan Yang, Mehak Arora, Ryan Zhang, Baraa Abed, Junseob Kim, Tilendra Choudhary, Md Hassanuzzaman, Kevin Zhu, Ayman Ali, Chengkun Yang, Alasdair Edward Gent, Victor Moas, Rishikesan Kamaleswaran
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.
By Siqi Li, Chuan Hong, Ziye Tian, Benjamin Sieu-Hon Leong, Koshi Nakagawa, Hideharu Tanaka, Sang Do Shin, Khuong Quoc Dai, Do Ngoc Son, Marcus Eng Hock Ong, Nan Liu, Molei Liu
Mr.Dec is a new Transformer‑decoder model that predicts 30‑day hospital readmission by treating each admission as a chronological sequence of daily multimodal events, integrating Electronic Health Record updates and Chest X‑ray findings. It uses disease‑specific supervised contrastive learning to shape a diagnosis‑aware latent space and preserves day‑level clinical signals that other methods often compress. Experiments on MIMIC‑IV and MIMIC‑CXR datasets show state‑of‑the‑art performance and the model can highlight "Critical Days" for actionable real‑time risk stratification.
By Minjun Kim, Jong Hak Moon
arXiv:2608. 13518v1 Announce Type: new Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint.
By Yunsung Chung, Yingshuo Liu, Abboud F. Hassan, Han Feng, Mary M. Maleckar, Nassir Marrouche, Jihun Hamm