ReviewComputational and structural biotechnology journal2017
Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.
Review in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 50 papers, 3 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.
Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
Randomized Controlled Multicenter Trial for the Validation of Vitalera Platform
Heart Failure Patient Management and Interventions Using Continuous Patient Monitoring Outside Hospitals and Real-world Data
Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it, 208 citations in OpenAlex.
- Economic evaluation of ferric carboxymaltose compared with placebo in iron-deficient patients with heart failure: a systematic review.International journal of clinical pharmacy · 2023Pooled it
- Health State Utilities of Patients with Heart Failure: A Systematic Literature Review.PharmacoEconomics · 2021Pooled it
- Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.BMC medical informatics and decision making · 2017Pooled it
- Cardiac Resynchronization Therapy With Defibrillator Using the JROAD-DPC Database: Cost-Effectiveness Analysis.Interactive journal of medical research · 2026Article
- Unveiling the gap in heart failure: a Brazilian Unified Health System study.Journal of global health · 2026Article
- Machine learning-based early identification of heart failure/congestion complicating acute coronary syndrome using body composition analysis: a retrospective cohort study.Frontiers in cardiovascular medicine · 2026Article
- Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records.Scientific reports · 2025Article
- Abnormal heart sound recognition using SVM and LSTM models in real-time mode.Scientific reports · 2025Article
- Development and validation of a nomogram to predict survival in septic patients with heart failure in the intensive care unit.Scientific reports · 2025Article
- Machine learning enhanced acute heart failure phenotype prediction using natural language processing and random forest.Frontiers in artificial intelligence · 2025Article
- Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients.Renal failure · 2024Article
- Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.Healthcare informatics research · 2024Article
- Machine learning in heart failure diagnosis, prediction, and prognosis: review.Annals of medicine and surgery (2012) · 2024Review
- Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.Physical and engineering sciences in medicine · 2024Article
- Review
- Time-domain heart rate variability features for automatic congestive heart failure prediction.ESC heart failure · 2024Article
- Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients.BMC cardiovascular disorders · 2024Article
- Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.Frontiers in medicine · 2024Article
- Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.Bioengineering (Basel, Switzerland) · 2023Article
- Personalized event prediction for Electronic Health Records.Artificial intelligence in medicine · 2023Article
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 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.
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.