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Related Experiment Video

Updated: Jan 22, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Desiderata for sharable computable biomedical knowledge for learning health systems.

Harold P Lehmann¹, Stephen M Downs²

¹Johns Hopkins University Baltimore Maryland.

Learning Health Systems

|June 28, 2019

Summary

This study outlines essential functions for sharing statistical models in clinical decision support systems. Influence diagrams are proposed as a framework to meet these requirements for effective knowledge sharing and deployment.

Keywords:

Bayesian analysis decision analysis decision support knowledge engineering predictive modeling

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Area of Science:

Health Informatics
Decision Science
Artificial Intelligence in Medicine

Background:

Learning health systems generate valuable knowledge objects based on statistical models.
Effective sharing of these knowledge objects is crucial for clinical decision support.
Current architectures may not fully support the desired functions for knowledge object sharing.

Purpose of the Study:

To define specific functions for sharing knowledge objects derived from statistical models.
To explore novel knowledge architectures for clinical decision support.
To demonstrate how influence diagrams can satisfy these desired functions.

Main Methods:

The study defines desiderata for knowledge object sharing, including local validation, threshold recalculation, explainability, and generalizability.
Influence diagrams, implemented using Bayesian networks, are proposed as a formal decision modeling approach.
The framework is evaluated for its ability to support primary knowledge artifact decisions and meta-decisions on deployment.

Main Results:

Influence diagrams meet the defined desiderata for sharing knowledge objects.
This approach supports both the creation and deployment decisions of clinical decision support tools.
The framework addresses challenges like semantic uncertainty and generalizability.

Conclusions:

Formal decision models, particularly influence diagrams, provide a robust framework for sharing statistical models in clinical decision support.
This architecture facilitates the integration of local preferences and ensures model transparency and reliability.
A research and development agenda is proposed to implement this framework.