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

Coronary Artery Disease I: Introduction

Coronary Artery Disease I: Introduction

Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...

Psychoneuroimmunology: Cardiovascular Disease

Psychoneuroimmunology: Cardiovascular Disease

Psychoneuroimmunology (PNI) is a multidisciplinary field that examines how psychological factors, particularly stress, interact with the immune system and impact physical health. Research in PNI has shown that chronic or traumatic stress can disrupt both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. These disruptions contribute to serious health conditions, including cardiovascular diseases.
A key area of focus in PNI is the relationship between stress and coronary...

Assessment of the Cardiovascular System I: Subjective Data

Assessment of the Cardiovascular System I: Subjective Data

A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...

You might also read

Related Articles

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

Sort by

Same author

Uncertainty analysis of spatiotemporal wake characteristics in groove-flap wind turbines based on turbulence intensity.

Scientific reports·2026

Same author

3D-printed metamaterial femoral prostheses via scalar field fusion and directional porous structure regulation.

BMC biotechnology·2026

Same author

The Impact of Digital Feedback Intervention on Digital Social Adaptation in Older Adults: A Randomized Controlled Study.

Journal of applied gerontology : the official journal of the Southern Gerontological Society·2026

Same author

pH-responsive alginate-inulin composite hydrogels incorporating pollen exine capsules for oral protein delivery: Structural characterization and controlled release mechanism.

International journal of biological macromolecules·2026

Same author

Optimization Design of High-Performance Powder-Spreading Arm for Metal 3D Printers.

Micromachines·2025

Same author

Effects of γ-aminobutyric acid and melatonin on quality, physiological-biochemical characteristics, cell ultrastructure and sugar metabolism of kiwifruit during postharvest shelf life.

Food chemistry·2025

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

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

Developing a machine learning-based predictive model for depression risk in patients with cardiovascular diseases.

Lin Zhang¹, Wenjing Li¹, Pengxin Fan¹

¹Department and Institute of Psychology, Ningbo University, Ningbo, 315211, China.

Journal of Affective Disorders

|March 25, 2026

Summary

Machine learning models can predict depression risk in cardiovascular disease (CVD) patients. The AdaBoost model identified life satisfaction and daily living activities as key predictors for early intervention.

Keywords:

Cardiovascular Diseases Depression Machine Learning Older Adults Risk Prediction Model

More Related Videos

An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents

An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents

Published on: December 2, 2015

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Related Experiment Videos

Last Updated: Mar 27, 2026

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

An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents

An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents

Published on: December 2, 2015

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Area of Science:

Medical Informatics
Public Health
Machine Learning in Healthcare

Background:

Cardiovascular diseases (CVD) often co-occur with depression, negatively impacting patient outcomes.
Accurate depression risk assessment is crucial for managing comorbid conditions in CVD patients.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting current depression risk in patients with CVD.
To identify key predictors for early depression risk identification in this population.

Main Methods:

Utilized data from the China Health and Retirement Longitudinal Study (CHARLS).
Trained and tested eight ML models (AdaBoost, XGBoost, RF, etc.) on 2020 data, with temporal validation using 2018 data.
Evaluated model performance using ROC/PR curves, calibration, DCA, and interpretability via SHAP analysis.

Main Results:

The Adaptive Boosting (AdaBoost) model demonstrated superior predictive performance.
Key predictors identified by SHAP analysis included life satisfaction, instrumental activities of daily living (IADL), sleep duration, and self-rated health.

Conclusions:

A validated ML model was developed to estimate depression risk in CVD patients.
This model can assist in the early identification of individuals at high risk for depression, facilitating timely interventions.