Related Experiment Video
Updated: Feb 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Discovering hidden knowledge through auditing clinical diagnostic knowledge bases
Matthew C Lenert1, Colin G Walsh2, Randolph A Miller2
1Dept. of Biomedical Informatics, Vanderbilt University, 2525 West End Ave. Suite 1475, Nashville, TN 37203, USA.
Objective:
Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation.
Materials And Methods:
Authors used unsupervised (Pearson's correlation - PC, Kendall's correlation - KC, and a heuristic algorithm - HA) methods to identify existing and discover new finding-finding interrelationships ("properties") in the INTERNIST-1/QMR KB. Authors estimated KB maintenance efficiency gains (effort reduction) of the approaches.
Results:
The methods discovered new properties at 95% CI rates of [0.1%, 5.4%] (PC), [2.8%, 12.5%] (KC), and [5.6%, 18.8%] (HA). Estimated manual effort reduction for HA-assisted determination of new properties was approximately 50-fold.
Conclusion:
Data mining can provide an efficient supplement to ensuring the completeness of finding-finding interdependencies in diagnostic knowledge bases. Authors' findings should be applicable to other diagnostic systems that record finding frequencies within diseases (e.g., DXplain, ISABEL).
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