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arXiv cs.AI· Soumya Ranjan Sahoo, Divya Bharti, Sanand Sasidharan·· 12 小时前AI 评分18

Onco-CONSTRUE 框架背后的纵向临床检索分析:按时间分配证据预算

Temporal Evidence Allocation For Longitudinal Clinical Retrieval: A Position And Technical Report

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该报告提出纵向临床检索应视为在时间上分配有限证据预算,而非仅按相关性排序,并拆分为时间证据分配、候选重建与临床验证三个目标。在 13 条临床查询、20 份病历的 Study A 中,Temporal-15 配置的里程碑槽位召回率达 0.538,高于 Global-50 基线的 0.388,但每条查询检索的证据单元为 496 对 50,增益不能单独归因于分配。

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Abstract:Electronic health records are written to support care, not retrieval. Across years of oncology notes, the primary diagnosis is restated in report after report, while the events that shape a patient's course, such as a recurrence or a change in therapy, may be recorded only once. A retriever that ranks passages by similarity to the query keeps returning the repeated material and can miss these transitions, that is, it exhibits temporal redundancy across historical narratives. We argue that longitudinal clinical retrieval should be treated as allocating a limited evidence budget across time, not just ranking by relevance. We separate the task into three objectives: temporal evidence allocation, candidate reconstruction, and clinical validation. In Study A (13 clinical queries over 20 patient records, with two-stage reconstruction), the Temporal-15 configuration reaches a milestone slot recall of 0.538, compared with 0.388 for the Global-50 baseline. However, it also retrieves far more evidence (496 versus 50 indexed units per query), so the gain cannot be credited to allocation alone. When evidence is held fixed, removing prompted precedence rules raises schema-order reversals from 0.010 to 0.080. A five-patient, single-query pilot with fixed budgets finds that wider temporal coverage does not reliably yield more dated milestones. Together, these results suggest that how much a record says about a period is a poor guide to how useful that period is as evidence. Because no expert reference timelines are available, our measurements describe retrieval behavior rather than clinical accuracy. This position and technical report presents the retrieval analysis behind the Onco-CONSTRUE framework and sets out a research agenda for budget-constrained longitudinal clinical retrieval.
Comments: Extended report of our accepted abstract at IEEE BHI 2026
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.27682 [cs.AI]
  (or arXiv:2610.27682v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.27682

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Divya Bharti Ms. [view email]
[v1] Tue, 20 Oct 2026 05:54:39 UTC (271 KB)

来源:arXiv cs.AI · arxiv.org