Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.

Journal of medical Internet research·2026

Same author

Chest Tube Learning Synthesis and Evaluation Assistant (CheLSEA): A Prospective Observational Trial of an Intelligent Decision Support System.

Innovations (Philadelphia, Pa.)·2026

Same author

Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.

Journal of medical Internet research·2026

Same author

Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.

The International journal of eating disorders·2025

Same author

Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.

Journal of the American Medical Informatics Association : JAMIA·2025

Same author

Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.

Journal of medical Internet research·2025

Search research articles

Related Experiment Video

Updated: Jul 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and

William Klement^1,2, Khaled El Emam^1,2

¹University of Ottawa, Ottawa, ON, Canada.

Journal of Medical Internet Research

|August 31, 2023

Summary

Inadequate reporting of machine learning (ML) studies hinders understanding and replication. This study consolidates ML reporting guidelines into a comprehensive checklist to improve prognostic and diagnostic study quality and reproducibility.

Keywords:

diagnostic machine learning model evaluation model training prediction models prognostic prognostic models reporting guidelines reproducibility guidelines

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jul 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Medical Informatics
Artificial Intelligence
Biostatistics

Background:

Reporting of machine learning (ML) prognostic and diagnostic studies is often insufficient, impeding comprehension and reproducibility.
Existing guidelines address specific aspects of the ML lifecycle but lack comprehensive coverage individually.

Purpose of the Study:

To consolidate existing ML reporting guidelines and checklists.
To create a comprehensive set of reporting items for prognostic and diagnostic ML studies in in-silico and shadow modes.

Main Methods:

Conducted a literature search identifying 192 articles on ML reporting guidance.
Screened and evaluated 17 high-quality source papers for reporting items.
Consolidated, refined through expert review, and validated reporting items into a checklist.

Main Results:

Identified 37 reporting items across 5 categories: study details, data, modeling, evaluation, and explainability.
No single source article covered all items; explainability and methodology had the least coverage.
Developed a checklist to facilitate more complete reporting.

Conclusions:

Existing guidelines are incomplete individually, necessitating a consolidated approach.
The developed checklist aims to enhance the quality and reproducibility of ML modeling studies.