arXiv AI By Miguel Contreras, Scott Siegel, Subhash Nerella, Jessica Sena, Jiaqing Zhang, Heng Sun, Hruday Tej Akkaladevi, Peiyu Lu, Jordan Rosen, Sumit Kapoor, Sasank Desaraju, Grace R. Thompson, Jacob Purcell, Michael Petrauskis, Philip KW. Hong, Meghan Brennan, Sarah Chrabaszcz, Tierra Smith, Ronnie Ren, Michel S. Kabbash, Ceyhun Haziroglu, Rushi Patel, Gabriel Gomez, Charlotte Chaiklin, Randy Leung, Kenneth N. John, Whitman Wiggins, Philip Kayser, Vincent Bird, Maria Bruzzone, Tyler J. Loftus, Azra Bihorac, Parisa Rashidi

Teaching LLMs How ICU Physicians Approach Clinical Reasoning Through OMOP-Aligned Retrieval Improves Reasoning Across Clinical Domains

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arXiv AI
Jul 21

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

arXiv:2505. 02722v2 Announce Type: replace Abstract: Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited.

By Junu Kim, Chaeeun Shim, Sungjin Park, Su Yeon Lee, Gee Young Suh, Chae-Man Lim, Seong Jin Choi, Song Mi Moon, Kyoung-Ho Song, Eu Suk Kim, Hong Bin Kim, Sejoong Kim, Chami Im, Dong-Wan Kang, Yong Soo Kim, Hee-Joon Bae, Sung Yoon Lim, Han-Gil Jeong, Edward Choi
arXiv AI
2d ago

Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

The paper introduces CARing, a framework that improves next‑visit diagnosis prediction by representing diagnoses with compositional Semantic IDs (SIDs) and optimizing multi‑label coverage through reinforcement learning. CARing encodes ontology‑enriched disease semantics into compact SIDs, aligns them with natural language and EHR contexts, and employs a coverage reward to encourage diverse diagnosis predictions. On MIMIC‑III and MIMIC‑IV datasets, CARing outperforms all EHR‑trained baselines in weighted F1 and achieves the highest top‑k recall, including R@30 scores above 46% in reasoning mode.

By Kaisong Zhang, Haotian Fang, Junmeng Zhou, Hang Lv, Yulan Pan, Yanchao Tan