Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study
Objective This study aims to reveal global advancements and trends in machine learning (ML) for chronic disease management through a comprehensive bibliometric analysis, identifying research priorities to guide deeper exploration in the future. Methods Relevant documents on ML and chronic disease ma...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251361614 |
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| Summary: | Objective This study aims to reveal global advancements and trends in machine learning (ML) for chronic disease management through a comprehensive bibliometric analysis, identifying research priorities to guide deeper exploration in the future. Methods Relevant documents on ML and chronic disease management were retrieved from the core Web of Science database. Visual analyses of publication volume, research institutions, and countries were conducted using CiteSpace, VOSviewer, RStudio, and other software. An expert panel further analyzed the scale, trends, and potential connections between various ML algorithms and chronic diseases. Results A total of 1,242 documents were included in this study. The findings indicate a continuous rise in studies on ML in chronic disease management, with the United States (n = 303, 23.5%) and China (n = 259, 20.1%) as primary research contributors. Logistic regression (n = 459) remains the most widely used algorithm, while neural networks (n = 183) show promising potential. Research hotspots are concentrated in diabetes and cardiovascular disease, focusing mainly on risk prediction, disease diagnosis, and personalized treatment. Conclusion ML is rapidly integrating into personalized medicine, real-time monitoring, and multimodal data fusion. However, challenges such as limited collaboration, weak model generalization, and data privacy persist. Future efforts should prioritize algorithm optimization and multisource data integration to advance clinical applications. |
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| ISSN: | 2055-2076 |