arXiv:2608.22062v1 Announce Type: new
Abstract: Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events....
By Xingtao Lin, Yubo Feng, Weixin Liu, Hangqi Ren, Junchao Zhou, Caiwan Sun, You Chen
CliniCIRCA is a modular large‑language‑model framework that reconstructs longitudinal mental‑health patient journeys from raw electronic health record narratives. It temporally classifies clinical events in unstructured discharge summaries without explicit timestamps, producing 15,891 tagged events from 52 summaries and correcting 629 errors to create verified gold‑standard timelines. The framework then generates temporally grounded summaries, compressing each source by 1.52×, and scales to produce 1,000 silver‑standard timelines for training, showing that instruction tuning improves event extraction, temporal tagging, and summarization across models.
By Aiwei Ivy Zhang, Nimra Ishfaq, Mohit Chandra, Santiago Alvarez Lesmes, Adam Coscia, Khatiya Chelidze Moon, Xiaohan Ding, Munmun De Choudhury
arXiv:2606. 05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts.
By Keqi Han, Ryan Young, Annabel Strauss, Lindsey Hughes, Katharine M. Nesbitt, Nicole Schueler, Che Ngufor, Carl Yang, Yuan Xue, Zhijun Yin
arXiv:2601.16753v2 Announce Type: replace-cross
Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is...
By Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Xin Chen
arXiv:2608.28974v1 Announce Type: new
Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
By Daniel Kang, Michelle Hu, Soorya Ram Shimgekar, Shayan Vassef, Yufan Wang, Anit Kumar Sahu, Munmun De Choudhury, Vedant Das Swain, Christian Poellabauer, Li Yan Khor, Koustuv Saha, Robert Wojciechowski, Elliot Kidd, Piyum Zonooz, Navin Kumar
The study evaluates large language models (LLMs) on unprocessed electronic medical record data for clinical registry abstraction, focusing on the American College of Cardiology National Cardiovascular Data Registry. In a pilot at one academic center, the LLM identified candidate data sources for each registry question, which abstractors used to define question‑specific document sets. In a subsequent validation at a second center, the LLM answered 157 registry questions with an overall mean accuracy of 91.5%, but accuracy dropped from 96% for simple medication or event flag questions to 62% for event timing questions, reflecting increasing ambiguity and required clinical reasoning.
By James Matheson, Betsy Castillo, Andrew Y. Shin, David Scheinker