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

Medical decision making based on inductive learning method

J Kern1, G Dezelić, T Dürrigl

  • 1Andrija Stampar School of Public Health, Medical School, University of Zagreb, Croatia.

Artificial Intelligence in Medicine
|June 1, 1993
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

The 12<sup>th</sup> International Conference on "Photosynthesis and Hydrogen Energy Research for Sustainability 2024": in honour of John Allen, Eva-Mari Aro, İbrahim Dinçer, Kazunari Domen, Elizabeth Gantt, and Andrey Rubin.

Photosynthetica·2025
Same author

Corrigendum to "Autoantibody status, neuroradiological and clinical findings in children with acute cerebellitis" [Eur. J. Paediatr. Neurol. 47 (2023) 118-130].

European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society·2024
Same author

Autoantibody status, neuroradiological and clinical findings in children with acute cerebellitis.

European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society·2024
Same author

Acute and Chronic Kernicterus: MR Imaging Evolution of Globus Pallidus Signal Change during Childhood.

AJNR. American journal of neuroradiology·2023
Same author

QL<sup>4</sup>MDR: a GraphQL query language for ISO 11179-based metadata repositories.

BMC medical informatics and decision making·2019
Same author

How to improve opportunistic screening by using EMRs and other data. The prevalence of undetected diabetes mellitus in target population in Croatia.

Public health·2017

Inductive learning, using decision trees, can aid medical decisions. Adjusting parameters and pruning are key for reliable results in clinical and epidemiological studies.

Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Biostatistics

Background:

  • Inductive learning methods are being explored for practical applications in clinical and epidemiological settings.
  • Decision trees are a common output of inductive learning algorithms.

Purpose of the Study:

  • To evaluate the effectiveness of inductive learning for medical decision-making.
  • To identify optimal parameters for inductive learning in specific medical contexts.

Main Methods:

  • Utilized a modified Quinlan's approach to construct decision trees based on attribute informativity.
  • Employed the ASSISTANT Professional software for inductive learning experiments.
  • Tested the methods on datasets related to rheumatoid arthritis and aging epidemiology.

Related Experiment Videos

Main Results:

  • Inductive learning parameter tuning is crucial for specific problems.
  • Tree pruning is recommended but may be insufficient alone for complex datasets.
  • Adjusting parameters like the minimal weight threshold can enhance solution quality.

Conclusions:

  • Inductive learning, with careful parameterization and pruning, shows promise as a decision support tool in clinical and epidemiological practice.
  • Further optimization of inductive learning parameters is necessary for robust medical decision-making.