Jove
Visualize
Contact Us

Related Experiment Videos

SEQUAL: an interactive computer program for sequential classification of biomedical data.

B Bona1, R Tempo, G Belforte

  • 1Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy.

Computer Methods and Programs in Biomedicine
|November 1, 1988
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

Pneumatic interactive gait rehabilitation orthosis: design and preliminary testing.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2011
Same author

The effect of outdoor production on the seroprevalence of Lawsonia intracellularis in growing-finishing pigs in a large pig production unit infected with endemic porcine proliferative enteropathy.

DTW. Deutsche tierarztliche Wochenschrift·2003
Same author

Staffieri tracheo-oesophageal prosthesis for voice rehabilitation after laryngectomy: an evaluation of characteristics.

Medical & biological engineering & computing·1999
Same author

Assessment of the hepatic circulation in humans: new concepts based on evidence derived from a D-sorbitol clearance method.

The Journal of laboratory and clinical medicine·1998
Same author

Dynamic behavior of the Provox and Staffieri prostheses for voice rehabilitation following total laryngectomy.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·1998
Same author

[General anesthesia without nitrogen monoxide. Its use in abdominal surgery].

Minerva anestesiologica·1993
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles
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

This study introduces a computer program for sequential Bayesian classification using probability density functions. A novel "probability of reversal" metric offers a more efficient stopping criterion for pattern classification tasks.

Area of Science:

  • Computer Science
  • Statistics
  • Machine Learning

Background:

  • Bayesian classification is a fundamental machine learning technique.
  • Sequential pattern classification requires efficient decision-making at each step.
  • Accurate probability density function estimation is crucial for Bayesian methods.

Purpose of the Study:

  • To describe a novel computer program for sequential Bayesian classification.
  • To introduce a new stopping criterion based on the 'probability of reversal'.
  • To evaluate the program's performance on simulated and real-world data.

Main Methods:

  • Utilizes non-parametric kernel probability density functions and a-priori class probabilities.
  • Employs a sequential approach where the best feature for measurement is computed at each step.

Related Experiment Videos

  • Implements Bayesian classification using the Bayes formula to assign patterns to classes.
  • Introduces and computes the 'probability of reversal' as a key metric.

    • Main Results:

    • The program effectively performs sequential Bayesian classification.
    • The 'probability of reversal' serves as an efficient stopping criterion.
    • The program demonstrated successful application on both simulated and real hepatic disease data.

    Conclusions:

    • The developed program offers an efficient method for sequential Bayesian classification.
    • The 'probability of reversal' provides a superior stopping rule compared to traditional methods.
    • The program's utility is validated in a practical medical application.