The study evaluates an auditable patient‑timeline reconstruction system that tracks provenance, records revisions, and refuses to answer when evidence is missing. Using a synthetic corpus of 1,000 patients and 3,353 notes, two provenance‑aware Evidence Graph operators reduced graph size by 33–37% while preserving all answers across 6,813 query points; a fixed‑window baseline failed to answer over half of the points. The system’s evidence‑gating mechanisms (BioClinicalBERT and a zero‑shot LLM) responded appropriately to evidence‑unavailable controls, but performance varied on marker‑free controls, with BERT maintaining high accuracy but the LLM’s coverage dropping sharply.
"whyItMatters":"The results demonstrate that provenance‑aware evidence graphs can significantly reduce data complexity while maintaining answer integrity, highlighting a practical approach to building auditable clinical NLP systems."
arXiv:2608. 07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, longitudinal records: determining what evidence is needed, retrieving and reconciling structured and free-text data, grounding conclusions in verifiable evidence, and deferring cases that cannot be resolved reliably.
By Veronica Chatrath, Bryan Zhu, George Pu, Jingxuan Fan, Apaar Shanker, Varun Ursekar, Anahita Sharma, Jason Qin, Keqi Han, Soham Dinesh Tiwari, Soham Dan, Vijay Kalmath, Yuan Li, Daniel Yue Zhang, Chenguang Wang, Zainab Doctor, Zhijun Yin, Nigam H. Shah, Yuan Xue
Grounded Adjudication of Variations across Extracted TimeLines (GAVEL) is a new LLM‑based protocol that compares two clinical timelines against their source case report, identifying discrepancy types, issuing verdicts, and citing relevant report passages for each difference. In a study of 126 reports, GAVEL evaluated 2,738 findings from GPT‑5.6sol and DeepSeek V3.2, ranked six LLM extractors and two human annotators, and guided a merging process that improved timeline accuracy—reducing discrepancies from 7.63 to 0.85 per report and yielding a 77.0% preference rate for merged timelines. The approach demonstrates that report‑based comparison can refine extracted timelines without assuming any single timeline as ground truth.
By Jack Cummins, Sayantan Kumar, Ketan Tamirisa, Jeremy C. Weiss
arXiv:2608.22899v1 Announce Type: new
Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquire...
By Xiwei Dai, Zijie Meng, Zhiting Fan, Yixuan Tang, Ziru Niu, Zuozhu Liu
arXiv:2609.21387v1 Announce Type: cross
Abstract: Relexicalization is a pivotal technique in clinical NLP, as it facilitates robust masking of sensitive information while synthesizing datasets that r...
By Dipankar Das, Atri Mandal, Sandeep Singh, Tushar Shandhilya
arXiv:2610.20512v1 Announce Type: new
Abstract: Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty...
By Jin-Jia Zheng, Vincent S. Tseng
arXiv:2608.15382v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy r...
By Ummara Mumtaz, Aimen Noor, Awais Ahmed
arXiv:2608. 05982v1 Announce Type: new Abstract: Inadequate target--disease linkage accounts for 40--50\% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients.
By Pui Chung Siu, Claudia Cabrera, Mani Mudaliar, Arkaitz Zubiaga
The paper introduces an uncertainty‑aware clinical knowledge graph for chest X‑ray device reasoning, capturing device instances, tip estimates, placement assessments, provenance, report events, and temporal links as interconnected evidence. The graph builder processes 30,083 studies from 3,255 patients, producing 914,632 evidence nodes and 884,549 typed relationships, while preserving detailed uncertainty and provenance information for each predicted device. The authors also outline typed data contracts, uncertainty representations, abstention rules, report‑image grounding, and longitudinal query mechanisms, though the current analysis is post‑hoc descriptive and does not yet demonstrate clinical utility.
By Harshil Lodhiya
arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.
By Ummara Mumtaz, Aimen Noor, Awais Ahmed
arXiv:2609.18852v1 Announce Type: new
Abstract: Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed t...
By Fengnan Li, Heman Burre, Liwen Sun, Roshni Varma, Matthew M. Engelhard
arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).
By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar