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Related Experiment Video

Updated: May 5, 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

Characterization of the biomedical query mediation process.

Gregory W Hruby¹, Mary Regina Boland, James J Cimino

¹Department of Biomedical Informatics, Columbia University, New York, NY 10032.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|December 5, 2013

Summary

Medical researchers often need help querying databases. Analyzing query dialogues reveals that data needs assessment is iterative, not a direct translation, informing better intelligent systems.

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Area of Science:

Biomedical Informatics
Data Science
Human-Computer Interaction

Background:

Medical researchers often find databases complex and require query analysts for data retrieval.
Mediated data queries are time-consuming, expensive, and do not scale effectively for research needs.

Purpose of the Study:

To analyze query mediation dialogues to inform the design of future intelligent query mediation systems.
To understand the negotiation process in formulating database queries for medical research.

Main Methods:

Recorded and transcribed 31 mediated query sessions from 22 research projects.
Developed an annotation schema for dialogue acts through iterative refinement.
Coded 3160 dialogue acts with three independent coders, assessing inter-rater agreement.

Main Results:

Identified key patterns and components within the query negotiation dialogue space.
Demonstrated that query formulation is an iterative process involving needs assessment and refinement.
Highlighted the complexity beyond a simple translation of researcher needs to database queries.

Conclusions:

The study provides foundational knowledge on the query negotiation process in medical research.
Findings suggest that intelligent systems should support iterative refinement and needs assessment.
Future systems can be designed to better assist researchers in navigating complex data retrieval.