Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 20, 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

Automatic medical knowledge acquisition using question-answering.

Emilie Pasche1, Douglas Teodoro, Julien Gobeill

  • 1Medical Informatics Service, University Hospitals of Geneva and University of Geneva, 1211 Geneva 14, Switzerland. emilie.pasche@sim.hcuge.ch

Studies in Health Technology and Informatics
|September 12, 2009
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A gold-standard French-language annotated corpus of oncological entities with ICD-O normalisation.

Scientific data·2026
Same author

Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.

BMJ health & care informatics·2026
Same author

Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

JMIR AI·2026
Same author

QT prolongation alerts lead to monitoring but rarely to therapeutic changes: a prospective hospital study.

Frontiers in pharmacology·2026
Same author

Can LLMs Turn French PET/CT Narrative Reports into Structured Knowledge?

Studies in health technology and informatics·2026
Same author

Explainable Framework for Ontology-Based Similarity: A Use Case on SNOMED CT.

Studies in health technology and informatics·2026
Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
See all related articles

This study introduces an automated method for generating drug prescription rules using a question-answering engine. The approach successfully extracts medical knowledge, aiding in the development of effective decision support systems.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Clinical decision support systems (CDSS) are crucial for drug prescription.
  • Manual rule creation for CDSS is time-consuming and labor-intensive.
  • Automating knowledge acquisition is essential for scalable CDSS development.

Purpose of the Study:

  • To propose a novel rule generation approach for automated acquisition of structured rules.
  • To enable the use of these rules in decision support systems for drug prescription.
  • To evaluate the effectiveness of text mining for extracting medical knowledge.

Main Methods:

  • A question-answering engine was employed to address specific information requests.
  • Rule generation was framed as an equation problem, using known rule components (e.g., disease, bacteria) as factors.

Related Experiment Videos

Last Updated: Jun 20, 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

  • The engine provided solutions (e.g., antibiotics) to complete the rules.

    • Main Results:

    • The rule generation method achieved a top precision of 0.64.
    • This indicates that approximately two-thirds of benchmark knowledge rules had one recommended antibiotic automatically acquired.
    • The approach demonstrated the feasibility of automatic text mining for medical knowledge extraction.

    Conclusions:

    • Automated rule generation using question-answering engines is a viable method for acquiring structured medical knowledge.
    • This approach can significantly contribute to the development of efficient drug prescription decision support systems.
    • Text mining offers a promising avenue for obtaining a substantial portion of medical knowledge.