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 Videos

Dynamic and static approaches to clinical data mining.

D McSherry1

  • 1School of Information and Software Engineering, University of Ulster, Coleraine, Northern Ireland, UK. dmg.mcsherry@ulst.ac.uk

Artificial Intelligence in Medicine
|May 4, 1999
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

Direct observation of strong coupling in a dense plasma.

Physical review. E, Statistical, nonlinear, and soft matter physics·2002
Same author

Avoiding premature closure in sequential diagnosis.

Artificial intelligence in medicine·1997
Same author

Obtaining medical information from the Internet.

Journal of the Royal College of Physicians of London·1997
Same author

Clinical problem solving by computer.

Journal of the Royal College of Physicians of London·1997
Same author

Albert's test: a neglected test of perceptual neglect.

Lancet (London, England)·1986
Same author

Knowledge acquisition in the development of an introductory guide to diagnosis in rheumatology.

Medical informatics = Medecine et informatique·1985
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
See all related articles

This study introduces dynamic data analysis for sequential diagnosis, aiding doctors in selecting optimal tests. It also presents an algorithm for identifying key diagnostic findings and their relationships.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support

Background:

  • Assessing diagnostic test utility requires considering the diagnostic strategy and prior evidence.
  • Sequential diagnosis necessitates a dynamic approach to data analysis for optimal test selection.
  • Static data analysis is useful for knowledge discovery, such as identifying key diagnostic findings.

Purpose of the Study:

  • To describe an intelligent program for sequential diagnosis using dynamic data analysis.
  • To present an algorithm for discovering features that provide evidence for or against a diagnosis.
  • To identify dominance relationships among features for enhanced diagnostic justification.

Main Methods:

  • Implementation of an intelligent program for sequential diagnosis based on physician evidence-gathering strategies.

Related Experiment Videos

  • Development of an algorithm to discover features that consistently support or refute a diagnosis.
  • Analysis of feature dominance to understand their relative diagnostic importance.

    • Main Results:

    • A dynamic approach to data analysis was implemented in an intelligent program for sequential diagnosis.
    • An algorithm successfully identified features providing definitive evidence for or against a selected diagnosis.
    • Dominance relationships among features were discovered, clarifying their comparative diagnostic value.

    Conclusions:

    • Dynamic data analysis is crucial for optimizing test selection in sequential diagnosis.
    • The developed algorithm effectively identifies critical diagnostic features and their relationships.
    • This approach enhances the ability to explain and justify diagnoses based on evidence.