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 Concept Videos

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sex and age differences of postural control in community-dwelling older adults.

Frontiers in human neuroscience·2026
Same author

Early Risk Factor Prediction in Chronic Kidney Disease Diagnosis Using Feature Selection and Machine Learning Algorithms.

Methods of information in medicine·2026
Same author

Comparison of machine learning methods in the early identification of vasculitides, myositides and glomerulonephritides.

Computer methods and programs in biomedicine·2023
Same author

Machine Learning Classification of Psychiatric Data Associated with Compensation Claims for Patient Injuries.

Methods of information in medicine·2023
Same author

Data analytics for cardiac diseases.

Computers in biology and medicine·2022
Same author

On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes.

Computer methods and programs in biomedicine·2021
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
Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

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

Related Experiment Video

Updated: Jul 4, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Machine learning method for knowledge discovery experimented with otoneurological data.

Kirsi Varpa1, Kati Iltanen, Martti Juhola

  • 1Department of Computer Sciences, FI-33014 University of Tampere, Tampere, Finland. Kirsi.Varpa@cs.uta.fi

Computer Methods and Programs in Biomedicine
|June 6, 2008
PubMed
Summary
This summary is machine-generated.

This study developed an otoneurological decision support system for vertigo diagnostics. Combining expert and machine-learned knowledge improved classification accuracy, highlighting the importance of attribute weighting for diagnosing vertigo diseases.

More Related Videos

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph
08:43

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph

Published on: May 29, 2026

Related Experiment Videos

Last Updated: Jul 4, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph
08:43

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph

Published on: May 29, 2026

Area of Science:

  • Otoneurology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Vertigo diagnostics can be complex, necessitating advanced decision support systems.
  • Current diagnostic methods may benefit from enhanced computational approaches.
  • Developing accurate inference mechanisms for otoneurological diseases is crucial.

Purpose of the Study:

  • To test the inference mechanism and knowledge discovery method of an otoneurological decision support system.
  • To evaluate the performance of a novel knowledge discovery method for vertigo diagnostics.
  • To compare machine-learned knowledge with expert knowledge and their combinations.

Main Methods:

  • Knowledge discovery based on attribute frequency distributions to form class patterns with weights and fitness values.
  • Testing the system using two vertigo patient datasets.
  • Comparing the developed system's classification accuracy against 1-nearest neighbor, 5-nearest neighbor, and Naive-Bayes classifiers.

Main Results:

  • Knowledge bases combining machine-learned and expert knowledge achieved the highest classification accuracies.
  • Attribute weighting significantly impacted the system's classification capability.
  • The knowledge discovery and inference methods demonstrated performance comparable to established classifiers for specific vertigo diseases.

Conclusions:

  • The integration of machine-learned knowledge with expert knowledge enhances diagnostic accuracy in otoneurological decision support systems.
  • Attribute weighting is a critical factor for optimizing classification performance.
  • The developed system shows promise for supporting the diagnosis of vertigo diseases.