ArticlePloS one2019
NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.BMC neurology · 2024Article
- Minimal amount of exercise prevents incident dementia in cognitively normal older adults with osteoarthritis: a retrospective longitudinal follow-up study.Scientific reports · 2023Article
- Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities.BMC medical informatics and decision making · 2023Article
- Applications of artificial intelligence in dementia research.Cambridge prisms. Precision medicine · 2023Review
- Development of a simple screening tool for determining cognitive status in Alzheimer's disease.PloS one · 2023Article
- Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.Journal of personalized medicine · 2022Article
- Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages.Frontiers in aging neuroscience · 2022Article
- Emergency department visits among people with predementia highly predicts conversion to dementia.PloS one · 2022Article
- Evaluation of Available Cognitive Tools Used to Measure Mild Cognitive Decline: A Scoping Review.Nutrients · 2021Article
- Motor Dysfunction Questionnaire and Dopamine Transporter Imaging Composite Scale Improve Differentiating Dementia With Lewy Bodies From Alzheimer's Disease With Motor Dysfunction.Frontiers in aging neuroscience · 2021Article
- Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale.Frontiers in aging neuroscience · 2021Article
- Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.Journal of medical Internet research · 2020Article
- Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.JAMIA open · 2020Review
- Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality.Frontiers in human neuroscience · 2020Article
- Machine Learning for the Preliminary Diagnosis of Dementia.Scientific programming · 2020Article
- Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning.IEEE journal of translational engineering in health and medicine · 2020Article
- Freezing of Speech Single Questionnaire as a Screening Tool for Cognitive Dysfunction in Patients With Dementia With Lewy Bodies.Frontiers in aging neuroscience · 2020Article
- An Informant-Based Simple Questionnaire for Visuospatial Dysfunction Assessment in Dementia.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors.
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Abstract
introductionUsing machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.
methodsWith the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group.
resultsThe screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. DISCUSSION: The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia.
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