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用于结构化临床数据分析的大型语言模型：双智能体接地与验证
论文提出 CLEAR-Med，一个用于结构化临床数据自然语言分析的双智能体框架：一个智能体将问题翻译为可执行 SQL 并保留查询与数据库结果生成草稿，另一个独立调用的跨供应商验证智能体结合确定性检查决定接受、请求一次有界修复或弃权。系统被形式化为有界选择性流水线，在包含 532 条去标识化婴儿记录、约 1300 个变量的 21 中心新生儿缺氧缺血性脑病表上评估。25 条开发基准查询重复五次，Invocation Agent 正确回答 83/125（66.4%，95% CI 48.0–83.2%），无接地 ChatGPT 基线为 15/125（12.0%，95% CI 3.2–22.4%），配对提升 54.4 个百分点。作者称该架构可让数值结论与已执行 SQL 保持可追溯，未解决情形可失败关闭；验证与弃权阶段的完整流水线前瞻评估留待后续工作。
AI推荐理由 arXiv 预印本，作者来自临床与 AI 交叉团队，方法描述具体（双智能体、SQL 接地、独立验证、可失败关闭），并给出 25 条查询基准上的量化结果（Invocation Agent 66.4% vs 无接地 ChatGPT 12.0%，配对提升 54.4 个百分点）。局限明确：验证与弃权阶段尚未做完整流水线前瞻评估，基准规模小（25 条查询、532 条记录），结果来自开发集而非独立测试集。对医疗结构化数据分析与 Agent 验证架构有参考价值，但需注意尚未经同行评审。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.34039 (cs)
[Submitted on 28 Sep 2026]
Title:Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation
Authors:Erfan D. Dehkalani, Seetha Shankaran, Abbot R. Laptook, C. Michael Cotten, P. Ellen Grant, Yangming Ou
View a PDF of the paper titled Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation, by Erfan D. Dehkalani and 5 other authors
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Abstract:Objective: To develop and characterize CLEAR-Med, a dual-agent framework for natural-language analysis of structured clinical data that separates SQL-based invocation from independent validation. Methods: CLEAR-Med uses one agent to translate a question into executable Structured Query Language (SQL), retain the executed query and database result, and produce a draft. Deterministic checks and a separately invoked cross-provider Validation Agent then accept the draft, request one bounded repair, or abstain. We formalized the system as a bounded selective pipeline and evaluated CLEAR-Med's configuration and scalability, and the Invocation Agent's accuracy and consistency on a 25-query development benchmark, using a harmonized 21-site neonatal hypoxic-ischemic encephalopathy table containing 532 de-identified infant records and approximately 1,300 variables. Results: CLEAR-Med completed all six nominal scalability configurations, including 500x1300. Across 25 development-benchmark queries repeated five times, the Invocation Agent answered 83 of 125 responses correctly (66.4%; query-cluster bootstrap 95% CI, 48.0-83.2%), compared with 15 of 125 (12.0%; 95% CI, 3.2-22.4%) for the ungrounded ChatGPT baseline, a paired improvement of 54.4 percentage points (95% CI, 36.8-72.0%). Conclusion: CLEAR-Med provides a general architecture for traceable analysis of structured clinical data: numerical claims remain linked to executed SQL, and unresolved cases can fail closed. The reported experiments characterize CLEAR-Med's configuration and scalability and the Invocation Agent's accuracy, while the formal analysis establishes the encoded-property guarantee of the complete control flow; a prospective full-pipeline evaluation of the validation and abstention stages is the next stage of this work.
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Artificial Intelligence (cs.AI)
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arXiv:2609.34039 [cs.AI]
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arXiv:2609.34039v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.34039
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From: Erfan Darzi [view email]
[v1]
Mon, 28 Sep 2026 00:02:51 UTC (473 KB)
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