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Updated: May 24, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

MINE: An Interactive Platform for Expert-Guided Medical Information Extraction.

Chuanming Dong1, Sandie Cabon1, Marc Cuggia1

  • 1Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

We developed MINE, a secure platform for creating clinical information extraction models. This expert-guided approach achieved over 95% accuracy for conditions like diabetes and hypertension.

Keywords:
Clinical TextInformation ExtractionLarge Language ModelsNatural Language Processing

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Published on: September 20, 2018

Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Clinical Data Management

Background:

  • Clinical reports are rich in patient data but challenging to analyze due to unstructured text and privacy concerns.
  • Existing methods for extracting information from clinical notes are often limited in scope or require extensive manual effort.
  • Developing automated, yet accurate, information extraction tools is crucial for leveraging clinical data for research and patient care.

Purpose of the Study:

  • To introduce MINE, a novel, secure, and interactive platform designed for collaborative development, evaluation, and deployment of clinical information extraction models.
  • To integrate diverse extraction methodologies, including rule-based, BERT-based, and large language models (LLMs), into a cohesive framework.
  • To demonstrate the effectiveness of expert-guided model development for accurate clinical data extraction.

Main Methods:

  • The MINE platform was designed as a unified environment integrating multiple information extraction techniques.
  • Experts collaboratively developed, trained, and validated models within the secure MINE interface.
  • The platform supports rule-based, BERT-based, and LLM-based approaches, allowing for flexible model creation.

Main Results:

  • Initial evaluations focused on extracting data for smoking status, diabetes, and hypertension.
  • The expert-guided extraction models developed on MINE achieved high performance, with F-scores exceeding 95% for these conditions.
  • These results validate the feasibility and effectiveness of the MINE platform for clinical information extraction.

Conclusions:

  • MINE offers a viable solution for overcoming the challenges of unstructured clinical data and privacy constraints.
  • Expert-guided development within the MINE platform enables the creation of highly accurate clinical information extraction models.
  • The platform's success in early trials suggests significant potential for improving clinical data utilization in healthcare and research.