ArticleDigital health
Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Advanced molecular detection, bioinformatics, and artificial intelligence era in medical laboratory science education.Journal of microbiology & biology education · 2026Article
- Convergence mapping analysis of interdisciplinary linkages in health services management.BMC health services research · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- Accelerating the performance of machine learning classifiers using bacterial colony optimization for heart disease prediction.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic 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 Socratic graph.