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Related Experiment Video

Updated: Jan 25, 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

An open access medical knowledge base for community driven diagnostic decision support system development.

Lars Müller¹, Rashmi Gangadharaiah², Simone C Klein³

¹Design Lab, UCSD, San Diego, CA, USA.

BMC Medical Informatics and Decision Making

|April 29, 2019

Summary

An open-access medical knowledge base was developed, showing comparable accuracy to existing diagnostic tools. This computable resource aids in developing new diagnostic assistants and improving healthcare technology.

Keywords:

Decision support systems, clinical (D020000)Diagnosis, differential (D003937)Knowledge bases (D051188)

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Modern diagnostic decision support systems increasingly use machine learning with large health datasets.
However, curated knowledge bases remain crucial for ground truth and explainable human-computer interaction.
Development of these systems is hindered by the scarcity of accessible, computable knowledge bases.

Purpose of the Study:

To construct an open-access knowledge base for developing prototype diagnostic decision support systems.
To evaluate the performance of this knowledge base against existing platforms.

Main Methods:

Developed an open-access knowledge base with over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications.
Utilized a modified set-covering algorithm for performance benchmarking.
Tested the system using 117 medical cases from literature and student materials.

Main Results:

The knowledge base contains over 8000 unique observations encoded with SNOMED or LOINC terms.
Accuracy was comparable to established tools like Isabel and DXplain, with the prototype suggesting the correct diagnosis as the top option in 33% of cases.
Identified development and evaluation shortcomings but confirmed the knowledge base as a promising platform.

Conclusions:

Successfully built and evaluated an open-access knowledge base to support the development of medical diagnostic assistants.
The knowledge base is expandable and curatable by users, serving as a foundation for technological advancement.
This resource facilitates new technology development and system improvement across various healthcare contexts.