ArticleFrontiers in aging neuroscience2022
Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Clinical prediction models using artificial intelligence approaches in dementia.Aging clinical and experimental research · 2025Pooled it
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal.BMC geriatrics · 2024Pooled it
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- Development and validation of a clinical prediction model for postoperative atrial fibrillation after lung cancer surgery: a machine-learning-based study.Frontiers in surgery · 2026Article
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- A simple nomogram to predict the onset of dementia in mild cognitive impairment: A retrospective study.Journal of Alzheimer's disease : JAD · 2025Article
- MMSE-Based Dementia Prediction: Deep vs. Traditional Models.Life (Basel, Switzerland) · 2025Article
- Detecting early stages of Alzheimer's disease using a web-based cognitive battery.GeroScience · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- A bibliometric analysis of the advance of artificial intelligence in medicine.Frontiers in medicine · 2025Article
- Personality and Transition From Mild Cognitive Impairment to Normal Cognition vs Dementia.Journal of the American Medical Directors Association · 2024Observational
- Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD): A Cohort for Dementia Research and Ethnic-Specific Insights.Dementia and neurocognitive disorders · 2024Article
- Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection.Brain informatics · 2024Review
- The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.Journal of personalized medicine · 2024Review
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- An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease.Journal of Alzheimer's disease reports · 2024Review
- A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting.Journal of Alzheimer's disease reports · 2024Article
- Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer's disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients' conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient. Methods: We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E ( Results: Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and Conclusion: We were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.
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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.