Back to search

Article

Heart Failure Prediction & Risk Stratification using Machine Learning

2026-04-05

Abstract excerpt

Heart failure (HF) is one of the most prevalent causes of morbidity, mortality, and healthcare expenditures, with approximately 6.7 million adults in the U.S. suffering from this condition and contributing to hundreds of thousands of deaths annually. Early diagnosis of high-risk individuals has been a challenge, as the HF-specific symptoms are often ignored or misinterpreted as normal aging, stress, or minor illne...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
04278dc8-761d-5f61-8194-0cbf1bb49bb7
DOI
10.64898/2026.04.03.26350139
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Heart Failure Prediction & Risk Stratification using Machine LearningDOI 10.64898/2026.04.03.26350139
Heart Failure Prediction & Risk Stratification using Machine LearningSemantic proximity
A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.Semantic proximity
Performance of Risk Models to Predict Mortality Risk for Patients with Heart Failure: Evaluation in an Integrated Health SystemSemantic proximity
Identifying subtypes of heart failure with machine learning: external, prognostic and genetic validation in three electronic health record sources with 320,863 individualsSemantic proximity
Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Semantic proximity
Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity
Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical UtilitySemantic proximity
Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort studySemantic proximity
Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudySemantic proximity
Enhanced machine learning and hybrid ensemble approaches for coronary heart disease predictionSemantic proximity
Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after dischargeSemantic proximity
An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare StudySemantic proximity
Select a neighboring publication to make it the new centre.