Cohort Discovery Query Optimization via Computable Controlled Vocabulary Versioning
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Updated: Apr 5, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Todd A Ferris1, Tanya Podchiyska1
1Stanford Center for Clinical Informatics, Stanford University, Stanford, CA, US.
Abstract:
Self-service cohort discovery tools strive to provide intuitive interfaces to large Clinical Data Warehouses that contain extensive historic information. In those tools, controlled vocabulary (e.g., ICD-9-CM, CPT) coded clinical information is often the main search criteria used because of its ubiquity in billing processes. These tools generally require a researcher to pick specific terms from the controlled vocabulary. However, controlled vocabularies evolve over time as medical knowledge changes and can even be replaced with new versions (e.g., ICD-9 to ICD-10). These tools generally only display the current version of the controlled vocabulary. Researchers should not be expected to understand the underlying controlled vocabulary versioning issues. We propose a computable controlled vocabulary versioning system that allows cohort discovery tools to automatically expand queries to account for terminology changes.
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