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

Turning medical data into decision-support knowledge

B F Bohren1, M Hadzikadic

  • 1UNC-Charlotte.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1994
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

Concept formation vs. logistic regression: predicting death in trauma patients.

Artificial intelligence in medicine·1996
Same author

Extracting knowledge from large medical databases: an automated approach.

Computers and biomedical research, an international journal·1995
Same author

Concept formation vs. logistic regression: predicting death in trauma patients.

Proceedings. Symposium on Computer Applications in Medical Care·1995
Same author

Information, intelligence, and interface: the pillars of a successful medical information system.

Medinfo. MEDINFO·1995

Physicians can use INC2.5, a computer-based assistant, for rapid second opinions and research analysis. This decision-support system builds predictive models from patient data, enhancing clinical decision-making.

Area of Science:

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

Background:

  • Physicians increasingly require advanced tools for rapid clinical decision-making.
  • Large datasets of patient information necessitate efficient analysis for research and practice.
  • Existing systems may lack user-friendliness or adaptability for clinical settings.

Purpose of the Study:

  • To introduce INC2.5, a novel computer-based decision-support system for physicians.
  • To enhance clinical decision-making through rapid second opinions and research analysis.
  • To provide an understandable and customizable tool for medical professionals.

Main Methods:

  • INC2.5 utilizes a decision tree approach based on previously seen patient data.

Related Experiment Videos

  • It employs a patient similarity matching algorithm for predictive modeling.
  • User customization of decision trees is a key feature for adaptability.

    • Main Results:

    • The system facilitates the prediction of new patient outcomes based on historical data.
    • INC2.5 provides a confidence factor to communicate environmental uncertainty.
    • The user-friendly design promotes adoption in clinical and research settings.

    Conclusions:

    • INC2.5 offers a valuable decision-support tool for physicians, improving diagnostic and research capabilities.
    • The system's intuitive design and customizable features enhance its practical utility.
    • The integration of confidence factors aids in managing the inherent uncertainty in medical data.