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

Heart Failure Drugs: Inhibitors of Renin-Angiotensin System

Heart Failure Drugs: Inhibitors of Renin-Angiotensin System

The activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS) contributes to cardiac remodeling, and inhibiting the RAAS is a pharmacological target in heart failure management. As a result, neurohumoral modulation is a crucial treatment principle for managing heart failure. This approach involves using medications like ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, mineralocorticoid receptor antagonists (MRAs), and neutral...

You might also read

Related Articles

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

Sort by

Same author

Graded Associations Between Left Ventricular Ejection Fraction Improvement and Cardiorenal Outcomes in Heart Failure With Improved Ejection Fraction.

Journal of the American Heart Association·2026

Same author

Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.

JMIR nursing·2026

Same author

Utility of deep learning for degree calculation of aortic arch calcification in chest-X ray.

BMC medical imaging·2026

Same author

Developmental effect modification of DEHP exposure and serum testosterone levels in a Taiwanese population-based cohort.

Environmental pollution (Barking, Essex : 1987)·2026

Same author

Integrated Chinese and Western Medicine for Breast Cancer Patients with Depression-Association with Survival and Healthcare Utilization: A Nationwide Retrospective Cohort Study in Taiwan.

Healthcare (Basel, Switzerland)·2026

Same author

Learning Analytics of a National Entrustable Professional Activities Platform: Cross-Sectional Study of System-Level Constraints on Advanced Entrustment in Competency-Based Medical Education.

JMIR medical education·2026

Search research articles

Related Experiment Video

Updated: May 15, 2025

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

Machine Learning in Predicting Cardiac Events for ESRD Patients: A Framework for Clinical Decision Support.

Chien-Wei Chuang^1,2, Chung-Kuan Wu^3,4,5, Chao-Hsin Wu^1,2

¹Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan.

Diagnostics (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

Machine learning models can predict major adverse cardiac events (MACEs) in patients with end-stage renal disease (ESRD). Key predictors include antiplatelet use, left ventricular hypertrophy, and serum albumin, enabling personalized treatment plans.

Keywords:

ESRD artificial intelligence machine learning medical decision-making risk factor

More Related Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

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

Related Experiment Videos

Last Updated: May 15, 2025

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

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

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

Area of Science:

Nephrology
Cardiology
Artificial Intelligence

Background:

Patients with end-stage renal disease (ESRD) face a heightened risk of major adverse cardiac events (MACEs).
Accurate risk prediction and tailored interventions are crucial for managing MACEs in ESRD patients.

Purpose of the Study:

To develop and evaluate machine learning (ML) models for predicting MACEs in ESRD patients.
To identify key predictive features for MACE risk.
To enhance clinical decision-making through improved risk assessment.

Main Methods:

Utilized CatBoost, XGBoost, and LightGBM on a dataset with 84 variables (demographics, labs, comorbidities).
Employed feature selection, cross-validation, and SHAP analyses for model interpretability.
Assessed model performance using AUC, sensitivity, and specificity.

Main Results:

CatBoost achieved the highest predictive performance with an AUC of 0.745.
Significant predictors of MACEs included antiplatelet use, left ventricular hypertrophy grade, and serum albumin.
SHAP analysis improved model interpretability, aiding clinician-led risk stratification.

Conclusions:

ML models show promise for enhancing MACE risk assessment in ESRD patients.
Integrating explainable AI into clinical workflows can support personalized treatment planning.
Future integration with EHR systems could enable real-time decision-making and improve patient outcomes.