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

Blocked arteries and multivariate regression.

D F Percy1

  • 1Department of Mathematics and Computer Science, University of Salford, England.

Biometrics
|September 1, 1992
PubMed
Summary

This study introduces a statistical model for predicting leg arterial occlusive disease severity using ultrasound blood flow data. The model provides cross-validated predictions, aiding in diagnosis and severity assessment.

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

Mixed multivariate generalized linear models for assessing lower-limb arterial stenoses.

Statistics in medicine·2001
Same author

Occupational hydrocarbon exposure and diabetic nephropathy.

Diabetic medicine : a journal of the British Diabetic Association·1994
Same author

Multivariate analysis of cholesterol distribution for monitoring the risk of coronary heart disease.

Statistics in medicine·1993
Same author

Renal impairment with chronic hydrocarbon exposure.

The Quarterly journal of medicine·1993
Same author

Primary glomerulonephritis and hydrocarbon exposure: a case-control study and literature review.

The Quarterly journal of medicine·1992
Same author

Detection of bone marrow abnormalities in patients with Hodgkin's disease by T1 mapping of MR images of lumbar vertebral bone marrow.

British journal of cancer·1992

Area of Science:

  • Medical imaging
  • Biostatistics
  • Vascular medicine

Background:

  • Ultrasound blood flow waveforms are crucial for diagnosing arterial occlusive disease in human legs.
  • Existing diagnostic methods may benefit from advanced statistical modeling for improved accuracy.

Purpose of the Study:

  • To develop and validate a statistical model for predicting arterial occlusive disease severity using ultrasound data.
  • To address challenges in analytical evaluation of Bayesian predictive density functions in complex models.

Main Methods:

  • Development of a statistical model based on seemingly unrelated regressions.
  • Utilizing a first-order approximation for the Bayesian predictive density function.
  • Employing cross-validation on a dataset of 31 patients for prediction accuracy assessment.

Main Results:

  • The developed statistical model successfully generated cross-validated predictions of disease severity.
  • The approach accounts for missing data components in training datasets.
  • The model's performance in predicting disease severity was evaluated.

Conclusions:

  • The statistical model offers a promising approach for predicting arterial occlusive disease severity from ultrasound data.
  • The approximation method allows for practical application of complex Bayesian models.
  • Further discussion on the implications of the results is warranted.

Related Experiment Videos