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Related Experiment Video

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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

Vaidurya: a multiple-ontology, concept-based, context-sensitive clinical-guideline search engine.

Robert Moskovitch¹, Yuval Shahar

¹Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University, P.O. Box 653, Beer Sheva 84105, Israel. robertmo@bgu.ac.il

Journal of Biomedical Informatics

|August 30, 2008

Summary

A new search engine, Vaidurya, enhances clinical guideline retrieval. Concept-based and context-sensitive searches significantly outperform traditional full-text search for better information access.

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Area of Science:

Medical Informatics
Information Retrieval
Clinical Decision Support

Background:

Digital clinical-Guideline Library (DeGeL) framework requires efficient information retrieval.
Existing full-text search methods for clinical guidelines are suboptimal.

Purpose of the Study:

To design and implement a generic search engine, Vaidurya, within the DeGeL framework.
To evaluate novel search methods for improved clinical guideline retrieval.

Main Methods:

Developed Vaidurya search engine with full-text, concept-based, and context-sensitive search functionalities.
Implemented concept-based search via pre-indexing guidelines.
Enabled context-sensitive search by semi-structuring guidelines using an ontology.

Main Results:

Vaidurya engine is fully functional and integrated into the DeGeL system.
Concept-based and context-sensitive ontology-independent searches significantly improve retrieval performance over free text search.
Both novel search methods demonstrated high feasibility in the clinical guideline domain.

Conclusions:

The Vaidurya search engine effectively enhances clinical guideline retrieval.
Concept-based and context-sensitive search methods offer significant advantages over traditional full-text search.
Ongoing development based on user feedback aims to further extend the system's capabilities.