VirtualTrialHarness：跨复杂临床数据模式协调虚拟试验协议的智能体运营化框架

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Abstract:Virtual trials use routinely collected clinical data to evaluate treatment strategies, but translating a study protocol into an executable analysis requires determining how clinical concepts and study requirements should be represented in the available data and applied consistently across the workflow. These decisions depend on local data representations and operational conventions and can involve source selection, patient and encounter identity, linkage, and temporal interpretation. Here we introduce VirtualTrialHarness (VTHarness), an agent framework that coordinates virtual-trial operationalization by selecting reusable procedural skills for each workflow stage and uses explicit contracts to maintain consistent study decisions across stages. We also introduce VTBenchmark for evaluating source selection and cohort construction. Across three complex synthetic environments, VTHarness successfully produced all required cohort outputs matching the references in 95.1% of workflows after requirement clarification, compared with 60.4% using retrieval from the same procedural corpus. Procedural skills improved source recovery across three language models and increased source overlap with analyst code in 13 Mayo Clinic cases. Site-specific adaptation enabled reference-matched cohort execution across eight MIMIC-IV tasks. Within the Mayo Clinic Virtual Trials Program, the framework supported investigator review of cohort construction and endpoint analysis. These findings support coordinating procedural guidance with workflow decisions to improve virtual-trial implementation across complex clinical schemas.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.05551 [cs.AI]
	(or arXiv:2611.05551v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2611.05551 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Pengze Li [view email]
[v1] Tue, 3 Nov 2026 19:45:21 UTC (1,441 KB)