ReviewFrontiers in neurology2025
Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024.
Review in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial intelligence in nursing practice for older adults with dementia: A narrative review informed by bibliometric mapping and implications for nurse-led research.Journal of Korean gerontological nursing · 2026Article
- Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study.Machine learning with applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: At present, the world is in the background of severe aging population challenges. Mild cognitive impairment (MCI), an intermediate state between normal aging and dementia, is a syndrome of cognitive impairment. Early recognition and intervention of MCI have great value for delaying the decline of cognitive function and improving the quality of life in the elderly. Machine learning (ML) is the core sub-branch direction in the field of artificial intelligence. In recent years, evaluating the potential application of machine learning in medicine has been popular, including the field of mild cognitive impairment. However, there is currently no bibliometrics to evaluate the scientific advances in this field. Objective: This study aims to visually analyze the current research trends regarding the application of machine learning in the field of MCI through bibliometry and visualization techniques. Methods: Using the Web of Science Core Collection database (Wo SCC), relevant articles and reviews of the collection database 2015-2024. Subsequently, the collected papers were subjected to bibliometric analysis utilizing CiteSpace, VOSviewer, and the "bibliometric" package in R language. Results: A total of 2056 papers related to machine learning in patients with MCI were retrieved from the Wo SCC database. The number of papers is increasing year by year. These papers are mainly from 9,577 organizations in 498 countries, most of which are from the United States and China. The journal with the largest number of publications is the FRONTIERS IN AGING NEUROSCIENCE. Folstein M is an authoritative author from the Johns Hopkins University School of Medicine. His paper "Mini-mental state: A practical method for grading the cognitive state of patients for the clinician" is the most cited article in this field. Literature and keyword analysis indicate that MCI prediction, automated monitoring of MCI, continuous evaluation and remote monitoring of cognitive function in individuals with MCI, and interdisciplinary data integration and personalized medicine are current research hotspots and development directions. Conclusion: This study is the first to use bibliometric methods to visualize and analyze the application field of machine learning in MCI, revealing research trends and frontiers in this field. This information will provide a useful reference for researchers focusing on machine learning applications in the field of MCI.
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.