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

A consultation system constructor for medical data analysis.

F De Rosis1, P Gissi, S Pizzutilo

  • 1Istituto di Scienze dell'Informazione, Universita, Bari, Italy.

International Journal of Bio-Medical Computing
|February 1, 1990
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

Refining instructional text generation after evaluation.

Artificial intelligence in medicine·1999
Same author

Generating recipient-centered explanations about drug prescription.

Artificial intelligence in medicine·1996
Same author

A general model of drug prescription.

Methods of information in medicine·1995
Same author

OPADE: optimization of drug prescription using advanced informatics.

Computer methods and programs in biomedicine·1994
Same author

Treatment of uncertainty in an oncology protocol by probabilistic and artificial intelligence approaches.

Methods of information in medicine·1988
Same author

[Analysis of epidemiological data using a personal computer: problems and integration of diverse software].

Epidemiologia e prevenzione·1987
Same journal

Commentary on a futuristic model of patient record systems and telemedicine.

International journal of bio-medical computing·1996
Same journal

Nonlinear eye movement detection method for drowsiness studies.

International journal of bio-medical computing·1996
Same journal

Segmentation of auditory brainstem response signals.

International journal of bio-medical computing·1996
Same journal

A comparison of neural network and Bayes recognition approaches in the evaluation of the brainstem trigeminal evoked potentials in multiple sclerosis.

International journal of bio-medical computing·1996
Same journal

Methodology for using the UMLS as a background knowledge for the description of surgical procedures.

International journal of bio-medical computing·1996
Same journal

An MLP-based model for identifying qEEG in depression.

International journal of bio-medical computing·1996
See all related articles

The MAD system aids expert data analysts in building reasoning models for tasks like epidemiology and image analysis. These models guide doctors in data interpretation, integrating expert knowledge and external programs seamlessly.

Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Expert systems are crucial for complex data analysis in specialized domains.
  • Bridging the gap between expert knowledge and clinical decision-making requires robust modeling tools.
  • Existing systems may lack flexibility in integrating diverse data analysis programs.

Purpose of the Study:

  • To introduce the MAD system, designed to assist expert data analysts.
  • To enable the creation of reasoning models for specific tasks in domains like epidemiology and image analysis.
  • To facilitate the guidance of medical doctors in analyzing domain-specific data.

Main Methods:

  • Expert knowledge is formalized at multiple levels, including domain descriptions and task-specific reasoning models.

Related Experiment Videos

  • Reasoning models are implemented using propositional calculus rules.
  • Meta-knowledge supports the knowledge acquisition process.
  • The system integrates external data analysis programs seamlessly, requiring no user training for them.

    • Main Results:

    • The MAD system effectively represents expert knowledge for building reasoning models.
    • It supports the integration of external data analysis tools, enhancing usability for medical professionals.
    • Demonstrated applications in epidemiology and image analysis showcase its practical utility.

    Conclusions:

    • MAD provides a flexible framework for expert data analysis and clinical decision support.
    • The system's design facilitates knowledge acquisition and the application of formalized reasoning.
    • Its ability to link with external software under MS-DOS makes it a versatile tool for specific analytical needs.