SynthesisHeart failure reviews2021
Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.
Synthesis in Heart failure reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 4 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis.Frontiers in cardiovascular medicine · 2022Pooled it
- Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.JACC. Advances · 2026Article
- Molecular Mechanisms and Multi-Omics Integration in Heart Failure: From Pathophysiology to Precision Medicine.International journal of molecular sciences · 2026Review
- Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.NPJ digital medicine · 2026Article
- A Lung Ultrasound Radiomics-Based Machine Learning Model for Diagnosing Acute Heart Failure in the Emergency Department.Diagnostics (Basel, Switzerland) · 2026Article
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- AI-assisted heart failure management: A review of clinical applications, case studies, and future directions.Global cardiology science & practice · 2025Review
- Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.European heart journal. Digital health · 2025Article
- Non-genetic factors determine deep learning identified ECG differences between black and white healthy subjects.NPJ cardiovascular health · 2025Article
- Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study.JMIR cardio · 2024Article
- Article
- Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.International journal of heart failure · 2024Review
- Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease.Bioengineering (Basel, Switzerland) · 2023Article
- Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.Bioengineering (Basel, Switzerland) · 2023Article
- A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction.Basic research in cardiology · 2023Review
- Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.Journal of geriatric cardiology : JGC · 2022Article
- Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
Machine learning (ML) algorithms "learn" information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.