Machine Learning in Heart Failure Research: A Bibliometric Analysis from 2003 to 2023

Abstract

Background Heart failure (HF) is a global public health problem with high morbidity and mortality. While machine learning (ML) has been perceived as a promising tool for HF research, a bibliometric analysis of this application is still lacking. This study aims to analyze the relevant papers from 2003 to 2023 and provide a comprehensive overview of this field in a visual way. Methods We systematically searched Web of Science Core Collection, PubMed, and preprint servers (arXiv, BioRxiv, and MedRxiv) to identify records from 2003 to 2023 on ML in HF research. After manual data cleansing, a Python approach based on the regular expression matching algorithm was used to automate text annotations, while three bibliometric analysis tools (CiteSpace, VOSviewer, and Bibliometrix) were used for the visualization of research trends, collaboration networks, and research hotspots. Results We analyzed 6,115 records (including 1,797 published papers) and observed a steady increase in annual publication rates since 2015, with a significant uptick after 2020. We identified 23 core journals in the field according to Bradford's law, and presented the top 10 journals with the highest citations, h-index, g-index, or m-index. The United States was the most productive country, followed by China and the United Kingdom. The most prolific institutions were Harvard Medical School and Mayo Clinic. Using text annotations, we identified 1,257 ML-related original HF research. In these studies, the main data modalities were ultrasound, electronic health records, and electrocardiograms. The most frequently applied ML methods were neural networks, followed by linear models and ensembles. The most common clinical goals were diagnosis, prediction, and classification. The main research topics included the classification of HF, AI-assisted medical diagnostic technologies, HF-related clinical prediction models, and HF-related bioinformatics research. Conclusions This bibliometric analysis revealed a significant growth in the application of ML in HF research over the past two decades. The current research landscape encompasses a wide range of ML techniques and applications, focusing on improving diagnosis, prognosis, classification, and precision treatment for HF patients.