ReviewEuropean heart journal. Digital health2022
Applications of artificial intelligence and machine learning in heart failure.
Review in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- 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.BMC medical informatics and decision making · 2026Pooled it
- Prognostic models in populations with heart failure: a systematic review and meta-analysis.Systematic reviews · 2026Pooled it
- Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.European heart journal. Digital health · 2026Review
- Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.Current heart failure reports · 2026Review
- Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.Scientific reports · 2026Article
- Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach.European journal of medical research · 2025Article
- Mapping the plasma metabolome to human health and disease in 274,241 adults.Nature metabolism · 2025Article
- From Clinic to Cloud: Efficacy of AI-Assisted Remote Monitoring of Patients With Implantable Cardiac Devices.Pacing and clinical electrophysiology : PACE · 2025Review
- Great debate: artificial intelligence will replace much of what cardiologists do.European heart journal · 2025Article
- Molecular Mechanisms of Cardiac Adaptation After Device Deployment.Journal of cardiovascular development and disease · 2025Review
- Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study.Age and ageing · 2025Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Artificial Intelligence in Diagnosis of Heart Failure.Journal of the American Heart Association · 2025Review
- Applications of Artificial Intelligence in Out-of-Hospital Cardiac Arrest: A Systematic Review.Cureus · 2025Review
- Machine learning in predicting heart failure survival: a review of current models and future prospects.Heart failure reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.
Indexed as
Identifiers
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.