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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.2K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.2K
Causality in Epidemiology01:21

Causality in Epidemiology

1.4K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.4K
Clinical Trials01:16

Clinical Trials

10.1K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
10.1K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

776
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
776
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

460
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
460
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

314
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
314

You might also read

Related Articles

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

Sort by
Same author

Risk assessment for canine periodontal disease using a hybrid causal Bayesian network.

Frontiers in veterinary science·2026
Same author

Response: "Letter to the Editor: Lessons to Be Learned From the COVID-19 Pandemic: Some Further Ideas".

International journal of public health·2026
Same author

"It's the future, come on!": a think aloud study exploring clinicians' use of knowledge-based AI decision support.

International journal of medical informatics·2025
Same author

What Lessons can Be Learned From the Management of the COVID-19 Pandemic?

International journal of public health·2025
Same author

Early clinical evaluation of a machine-learning system for risk prediction of trauma-induced coagulopathy in the prehospital setting.

Emergency medicine journal : EMJ·2025
Same author

A Bayesian Network model of pregnancy outcomes for England and Wales.

Computers in biology and medicine·2025

Related Experiment Video

Updated: Dec 16, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K

Medical idioms for clinical Bayesian network development.

Evangelia Kyrimi1, Mariana Raniere Neves1, Scott McLachlan1

  • 1Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.

Journal of Biomedical Informatics
|July 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces reusable medical reasoning patterns, called medical idioms, to improve the development of Bayesian Networks (BNs) for medical applications. These patterns enhance clarity and structure in medical BN models.

Keywords:
Bayesian networksKnowledge elicitationMedical idiomsReasoning patterns

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.8K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.1K

Related Experiment Videos

Last Updated: Dec 16, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.8K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.1K

Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Bayesian Networks (BNs) are widely used in medical applications.
  • Current methods for developing medical BNs lack standardized structure development and justification.
  • This often results in ad hoc network construction with limited methodological improvement.

Purpose of the Study:

  • To propose generally applicable and reusable medical reasoning patterns (medical idioms) for developing medical BNs.
  • To extend the idiom-based approach for interventional and counterfactual reasoning in medical contexts.
  • To improve the clarity and structure of medical BN models.

Main Methods:

  • Development of specific medical idioms, extending the generic idiom approach.
  • Application of medical idioms to coronary artery disease examples.
  • Extension of idioms to represent interventional and counterfactual reasoning.

Main Results:

  • Proposed medical idioms provide logical, reusable patterns for Bayesian Network construction.
  • Illustrated application to coronary artery disease and other ongoing medical BN projects.
  • Demonstrated improved model structure in published BN models using medical idioms.

Conclusions:

  • Medical idioms offer a structured and reusable methodology for building medical Bayesian Networks.
  • The proposed patterns enhance clarity, justification, and methodological rigor in medical BN development.
  • This approach facilitates the creation of more robust and interpretable medical AI models.