ReviewCurrent heart failure reports2020
Big Data Approaches in Heart Failure Research.
Review in Current heart failure reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 23 citations in OpenAlex.
- Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.Journal of human hypertension · 2025Observational
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.International journal of heart failure · 2024Review
- Approaching a nationwide registry: analyzing big data in patients with heart failure.Turkish journal of medical sciences · 2024Article
- Charting a roadmap for heart failure research in India: Insights from a qualitative survey.The Indian journal of medical research · 2023Article
- GENERATOR HEART FAILURE DataMart: An integrated framework for heart failure research.Frontiers in cardiovascular medicine · 2023Article
- Big Data in Cardiology: State-of-Art and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- Endotyping in Heart Failure - Identifying Mechanistically Meaningful Subtypes of Disease.Circulation journal : official journal of the Japanese Circulation Society · 2021Review
- Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.Journal of clinical hypertension (Greenwich, Conn.) · 2021Article
- Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThe goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential. RECENT
findingsOmics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.