ArticleBMC medical informatics and decision making2023
Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 18 citations in OpenAlex.
- Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.Clinical and experimental medicine · 2026Article
- Prognostic Value of HALP and AHEAD Scores for Predicting 1-Month Heart Failure Following Myocardial Infarction.Journal of clinical medicine · 2026Article
- Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Impact of sacubitril/valsartan on cardiac structure and function in post-acute myocardial infarction heart failure patients aged over 65: a single-center experience.American journal of translational research · 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 to identify heart failure with preserved ejection fraction in type 2 diabetes mellitus patients.Frontiers in cardiovascular medicine · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Single-cell RNA sequencing reveals the potential role of Postn(+) fibroblasts in promoting the progression of myocardial fibrosis after myocardial infarction.Scientific reports · 2025Article
- Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction.BMC cardiovascular disorders · 2025Article
- Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.BMC psychiatry · 2025Article
- Are we ready to integrate advanced artificial intelligence models in clinical laboratory?Biochemia medica · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction.Frontiers in cardiovascular medicine · 2025Article
- Machine learning in the prevention of heart failure.Heart failure reviews · 2025Review
- Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters.Frontiers in cardiovascular medicine · 2025Article
- Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model.Frontiers in cardiovascular medicine · 2025Article
- Construction of a Clinical Prediction Model for Complications After Femoral Head Replacement Surgery.Journal of clinical medicine research · 2024Article
- Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features.International journal of general medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsHeart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms.
methodsCohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively.
resultsThe best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits.
conclusionsThis study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.
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.