arXiv:2606. 10725v1 Announce Type: new Abstract: Background.
By Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov
arXiv:2606. 24102v1 Announce Type: cross Abstract: Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values.
By Lin Lawrence Guo, Adam Paul Yan, Emily Vettese, Lillian Sung
This study develops a rolling day‑wise mortality prediction model for critically ill patients with acute kidney injury on continuous renal replacement therapy (CRRT) by incorporating minute‑level machine pressure waveforms alongside electronic health record (EHR) data. After cleaning the raw machine data—aligning it with therapy intervals, removing priming and downtime minutes, and denoising artifacts—the authors trained a transformer‑based stacked ensemble that fuses a sequence transformer with classical models using circuit‑instability features and clinical variables. In a multi‑center benchmark of 976 patients, the combined model achieved a one‑day mortality AUROC of 0.766, outperforming machine‑only (0.625) and EHR‑only (0.717) models, and SHAP analysis highlighted key pressure‑related features such as filter pressure, transmembrane pressure, and access‑to‑return difference.
whyItMatters":"By integrating previously discarded CRRT machine data, the study provides a continuous, real‑time risk signal that could enable earlier detection of patient deterioration and improve mortality prediction in a high‑risk population."
By Shehan Irteza Pranto, Joanna Yang, Joshua Lambert, Stuart L. Goldstein, Lili Chan, Girish N. Nadkarni, Tiago K. Colicchio, Javier A. Neyra, Jin Chen
arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.
By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab
arXiv:2511. 02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure.
By Yohan Lee, Dong Gyun Kang, SeHoon Park, Sa-Yoon Park, Kwangsoo Kim
The study benchmarks tokenization choices for generative medical event models, evaluating quantization granularity, reference-range anchoring, code–value fusion, numeric and temporal encodings, and native versus harmonized event representations. Using Llama and Qwen architectures, 156 models were trained and assessed on early hospitalization data, showing that fusing codes with value deciles and using event-order or admission-relative RoPE embeddings improved predictive performance. The Common Longitudinal Intensive Care Unit Data Format (CLIF) reduced token count by 30.8% while enhancing outcomes in most families.
By Inhyeok Lee, Luke Solo, Michael C. Burkhart, Bashar Ramadan, Sahil Sethi, Sarah Jabbour, William F. Parker, Brett K. Beaulieu-Jones