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

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Good things come in threes: evaluating clinical utility of machine learning-derived clusters.

Daniil Lisik^1,2, Jip W T M De Kok^3,4, Nicolás Bermúdez Barón²

¹Department of Public Health and Clinical Medicine, The OLIN and Sunderby Research Unit, Umeå University, Umeå, Sweden.

|June 15, 2026

Summary

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

Serum and Nasal Lavage Fluid Eosinophil-Derived Neurotoxin Levels in Clinically Defined Asthma Phenotypes.

Clinical and translational allergy·2026

Same author

Transitions to and reversion from a persistent frequent-exacerbator state in COPD.

ERJ open research·2026

Same author

Standardising terminology in routine electronic health records for respiratory endpoints: why we still can't agree.

Thorax·2026

Same author

Parkinsonian signs and cognitive trajectories in older adults: a population-based longitudinal study.

Age and ageing·2026

Same author

Stability of Type 2 Inflammation in a 12-Year Adult-Onset Asthma Follow-Up Study.

Journal of asthma and allergy·2026

Same author

Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.

Sleep & breathing = Schlaf & Atmung·2026

This study proposes a framework to ensure machine learning (ML) clusters are clinically useful. It addresses limitations in current ML applications for disease subtyping, aiming for better real-world healthcare integration.

Area of Science:

Medical Informatics
Computational Biology
Clinical Data Science

Background:

Machine learning (ML) cluster analysis is widely used for medical condition subtyping.
Increasing data and algorithmic advances boost ML model use in healthcare.
Lack of guidelines hinders practical implementation due to methodological and reporting issues.

Purpose of the Study:

To propose a general framework for assessing ML-derived cluster clinical utility.
To provide guidelines for evaluating ML-generated subgroups in medicine.

Main Methods:

The framework focuses on three key domains for assessing ML-derived clusters.
Domains include clinical relevance, stability, generalizability, and ease of identification.

Keywords:

Artificial intelligence cluster analysis clusters machine learning phenotypes

Related Experiment Videos

Last Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Main Results:

The proposed framework offers a structured approach to evaluate ML-generated subgroups.
It aims to bridge the gap between ML methodology and clinical application.

Conclusions:

Implementing this framework can enhance the clinical utility of ML-derived clusters.
Standardized assessment is crucial for reliable ML application in disease subtyping.