ArticleNutrients2022
Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Association between pulse pressure levels and mortality in patients with septic shock: a retrospective cohort study.Frontiers in medicine · 2026Article
- AI analysis for ejection fraction estimation from 12-lead ECG.Scientific reports · 2025Article
- Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.NPJ digital medicine · 2025Review
- Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Leveraging ECG images for predicting ejection fraction using machine learning algorithms.Indian heart journalArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is estimated that 360,000 patients have suffered from heart failure (HF) in Taiwan, mostly those over the age of 65 years, who need long-term medication and daily healthcare to reduce the risk of mortality. The left ventricular ejection fraction (LVEF) is an important index to diagnose the HF. The goal of this study is to estimate the LVEF using the cardiovascular hemodynamic parameters, morphological characteristics of pulse, and bodily information with two machine learning algorithms. Twenty patients with HF who have been treated for at least six to nine months participated in this study. The self-constructing neural fuzzy inference network (SoNFIN) and XGBoost regression models were used to estimate their LVEF. A total of 193 training samples and 118 test samples were obtained. The recursive feature elimination algorithm is used to choose the optimal parameter set. The results show that the estimating root-mean-square errors (E
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Registered trials
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