arXiv cs.AI· Sayantan Kumar, Nicolas Grimaldi, Jack Cummins, Jeremy C. Weiss·· 1 天前AI 评分27
基于 UID 保留的多模态重建与来源溯源裁决：将临床事件锚定在时间线上
Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication
AI 导读
研究者提出一种保留 UID 的临床时间线重建方法，为每个抽取事件分配与来源文本片段绑定的持久唯一标识，并联合修正事件时间估计。在 40 份临床医生标注的出院小结上，该流程在相同骨干模型下比 LLM-Timeline 和 TKW2 找回更多参考事件；对 GLM-5.2 而言，加入结构化证据提升了时间戳一致性且事件召回基本不变。
正文 · 原文
Abstract:Interpreting a patient's hospital course depends on when symptoms developed, tests were performed, and treatments began. Discharge summaries often leave this chronology unclear, while structured electronic health record (EHR) tables timestamp only some of these events. We reconstruct clinical timelines by retrieving structured evidence for narrative events and jointly revising their estimated times. Each extracted occurrence carries a persistent unique identifier (UID) linked to its source span, keeping repeated mentions distinct throughout reconstruction. We also introduce GAVEL, a large language model (LLM) judge that checks disagreements between two timelines against the note and structured record and cites evidence for its verdicts. We evaluated six open-weight models on 40 clinician-annotated discharge summaries. Our pipeline recovered more reference events than LLM-Timeline and TKW2 when each comparison used the same backbone. For GLM-5.2, adding structured evidence improved timestamp agreement with essentially unchanged event recovery; ablations showed that links to timestamped source rows contributed to this improvement. Results varied across the other models, with some timing gains accompanied by fewer recovered events. Both GAVEL judges, GLM-5.2 and MiMo-V2.6-Pro-RL, favored multimodal over text-only GLM-5.2 timelines, while neither clearly distinguished the DeepSeek V3.2 variants. Manual review upheld most sampled GAVEL findings. The source links allow reconstructed times to be checked, and GAVEL identifies disagreements for human review.
| Comments: | Accepted as Proceedings of Machine Learning for Health (ML4H 2026) in PMLR |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.13062 [cs.AI] |
| (or arXiv:2609.13062v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13062 arXiv-issued DOI via DataCite |
Submission history
From: Sayantan Kumar [view email]
[v1]
Fri, 11 Sep 2026 17:01:10 UTC (10,331 KB)
[v2]
Wed, 11 Nov 2026 17:19:45 UTC (2,110 KB)
来源：arXiv cs.AI · arxiv.org