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https://irlib.pccu.edu.tw/handle/987654321/44640
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| Title: | NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia |
| Authors: | Chiu, PY (Chiu, Pai-Yi)
Tang, HP (Tang, Haipeng)
Wei, CY (Wei, Cheng-Yu)
Zhang, CY (Zhang, Chaoyang)
Hung, GU (Hung, Guang-Uei)
Zhou, WH (Zhou, Weihua) |
| Contributors: | 運動與健康促進學系 |
| Keywords: | ALZHEIMERS ASSOCIATION WORKGROUPS
AD8 INFORMANT INTERVIEW
DIAGNOSTIC GUIDELINES
NATIONAL INSTITUTE
CUTOFF SCORES
RELIABILITY
VALIDITY
DISEASE
VERSION
RECOMMENDATIONS |
| Date: | 2019-05-08 |
| Issue Date: | 2019-06-25 12:42:57 (UTC+8) |
| Abstract: | Introduction
Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia.
Methods
With 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.
Results
The 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. |
| Relation: | PLoS ONE 14(3): e0213430 |
| Appears in Collections: | [運動與健康促進學系] 期刊論文
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