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

Models of Health Promotion and Illness Prevention I

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...

You might also read

Related Articles

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

Sort by

Same author

Novel Mannich based derivatives of isoliquiritigenin and liquiritigenin, bioactive constituents of <i>Glycyrrhiza glabra</i>, as potent antimalarial agents.

Natural product research·2026

Same author

Association of PARP1 SNP (rs1136410) with Brain Tumor Risk: Insights from Khyber Pakhtunkhwa.

Asian Pacific journal of cancer prevention : APJCP·2026

Same author

Exploring Mixed Feeding Practices Among Indian Mothers: Knowledge, Attitudes, and Reported Barriers to Exclusive Breastfeeding.

Cureus·2026

Same author

Sarcopenic Obesity in Indian Adults with Type 2 Diabetes Mellitus: Prevalence and Associated Risk Factors.

Indian journal of endocrinology and metabolism·2026

Same author

Expression of asprosin and OLFR734 in reproductive tissues of polycystic ovary syndrome mice: insights into metabolic and reproductive dysfunction.

Cell and tissue research·2026

Same author

Subclavian chronic total occlusion angioplasty facilitates cardiac implantable electronic device upgrades: A single-center case series.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2026

Search research articles

Related Experiment Video

Updated: May 8, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

Machine Learning-Based Prediction Models for Healthcare Outcomes in Patients Participating in Cardiac Rehabilitation:

Xiarepati Tieliwaerdi¹, Kathryn Manalo, Abulikemu Abuduweili

¹Author Affiliations: Department of Medicine, Allegheny Health Network, Pittsburgh, Pennsylvania (Drs Tieliwaerdi, Manalo, Khan, and Appiah-kubi); Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania(Dr Abuduweili); and Allegheny Health Network, Allegheny Health Network Cardiovascular Institute, Pittsburgh, Pennsylvania (Drs Williams and Oehler).

Journal of Cardiopulmonary Rehabilitation and Prevention

|April 21, 2025

Summary

Machine learning (ML) models show promise for predicting outcomes in cardiac rehabilitation (CR). However, current models lack external validation and robust methodology, questioning their readiness for clinical practice.

Keywords:

cardiac rehabilitation clinical prediction models machine learning systematic review

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 8, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Cardiology
Medical Informatics
Machine Learning

Background:

Cardiac rehabilitation (CR) significantly reduces mortality and morbidity in cardiovascular disease patients.
Machine learning (ML) is increasingly applied to predict healthcare outcomes, including within CR.

Purpose of the Study:

To critically appraise existing ML-based prognosis predictive models in CR.
To identify research gaps in ML applications for CR outcome prediction.

Main Methods:

Systematic literature search across major databases (Scopus, PubMed, Web of Science, Google Scholar) up to January 2024.
Extracted data on clinical features, outcomes, model development, validation, and performance metrics.
Assessed study quality using IJMEDI and Prediction Model Risk of Bias Assessment Tool.

Main Results:

22 ML models from 7 studies were analyzed, mostly with small patient cohorts (41-227).
Models predicted various CR progression stages with good performance (AUC 0.82-0.91, sensitivity 0.77-0.95).
No models underwent calibration or external validation; most had bias concerns.

Conclusions:

Current ML models for CR show potential but are not ready for clinical implementation due to lack of validation and bias.
Further research requires external validation of existing models and development of new models using robust methods on larger datasets.
Future models should target diverse clinical outcomes within CR to enhance predictive capabilities.