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

The EMG diagnosis--an interpretation based on partial information.

M Suojanen1, S Andreassen, K G Olesen

  • 1Department of Medical Informatics and Image Analysis, Institute of Electronic Systems, Aalborg University, Denmark. marko.suojanen@orion.fi

Medical Engineering & Physics
|January 7, 2000
PubMed
Summary

This study resolves a diagnostic paradox by incorporating patient referral information, known as Found In Doctor

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

The Education in Medical Informatics.

Yearbook of medical informatics·2016
Same author

Image and Signal Processing.

Yearbook of medical informatics·2016
Same author

Quantifying the associations between antibiotic exposure and resistance - a step towards personalised antibiograms.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2016
Same author

Modelling and control in biomedical systems.

Computer methods and programs in biomedicine·2011
Same author

Symposium on Modelling and Control in Biomedical Systems, Aalborg, Denmark, 2009. Introduction.

Computer methods and programs in biomedicine·2011
Same author

EMG-torque dynamics at different contraction levels in human ankle muscles.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2010

Area of Science:

  • Neurology
  • Medical Diagnostics
  • Biostatistics

Background:

  • A significant discrepancy exists between general disease prevalence and prevalence within electromyography (EMG) laboratory populations.
  • Prior probabilities for disease diagnosis in EMG settings often overlook crucial patient-specific pre-referral data.
  • EMG diagnosis alone provides a limited view, necessitating a broader data integration approach.

Purpose of the Study:

  • To resolve the paradox of differing disease prevalences in general versus EMG-specific populations.
  • To enhance diagnostic accuracy by integrating pre-referral patient information into EMG expert systems.
  • To introduce a novel method for incorporating clinical and laboratory findings into diagnostic models.

Main Methods:

  • Proposed a solution using stochastic variables, termed Found In Doctor's Lab (FIDL) factors, to summarize referral information.

Related Experiment Videos

  • Integrated FIDL factors into the EMG expert system MUNIN.
  • Validated the approach using 30 previously evaluated patient cases.

    • Main Results:

    • The proposed method significantly improved the specificity of EMG-based diagnoses.
    • Diagnostic sensitivity remained unaffected by the integration of FIDL factors.
    • Demonstrated a more comprehensive diagnostic capability by including pre-referral data.

    Conclusions:

    • Incorporating Found In Doctor's Lab (FIDL) factors enhances EMG diagnostic specificity.
    • This approach offers a more complete patient view, resolving prior probability paradoxes.
    • The method shows promise for improving the overall performance of diagnostic expert systems.