Temporary Disabled. :) please Go back Author:("Kim, Hairin") - IOS Press www.fgks.org » Address: [go: up one dir, main page] Include Form Remove Scripts Accept Cookies Show Images Show Referer Rotate13 Base64 Strip Meta Strip Title Session Cookies You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly. Go to headerGo to navigationGo to searchGo to contentsGo to footer In header section. Select this link to jump to navigation Help About us Contact us In navigation section. Select this link to jump to content Menu Home Journals Cart € title="Change currency to USD - US Dollar"$ Log in / Register Search Search Published between: Published from year: and Published to year: Search syntax help Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you. Boolean operators This OR thatThis AND thatThis NOT that Must include "This" and "That" This That Must not include "That" This -That "This" is optional This +That Exact phrase "This That" "This That" Grouping (this AND that) OR (that AND other) In content section. Select this link to jump to navigation Your search for: 'author:("Kim, Hairin")' has returned 1 result. (0.008s) Save search Mark all Add marked to cart Export marked citations Select this result for bulk action Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer’s Disease Authors: Kwak, Seyul | Oh, Dae Jong | Jeon, Yeong-Ju | Oh, Da Young | Park, Su Mi | Kim, Hairin | Lee, Jun-Young Article Type: Research Article Abstract: Background: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. Objective: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. Methods: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional …impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. Results: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. Conclusion: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia. Show more Keywords: Dementia, functional status, machine learning, neuropsychological tests DOI: 10.3233/JAD-215244 Citation: Journal of Alzheimer's Disease, vol. 85, no. 3, pp. 1357-1372, 2022 Get PDF No filters available.
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Authors: Kwak, Seyul | Oh, Dae Jong | Jeon, Yeong-Ju | Oh, Da Young | Park, Su Mi | Kim, Hairin | Lee, Jun-Young
Article Type: Research Article
Abstract: Background: In assessing the levels of clinical impairment in dementia, a summary index of neuropsychological batteries has been widely used in describing the overall functional status. Objective: It remains unexamined how complex patterns of the test performances can be utilized to have specific predictive meaning when the machine learning approach is applied. Methods: In this study, the neuropsychological battery (CERAD-K) and assessment of functioning level (Clinical Dementia Rating scale and Instrumental Activities of Daily Living) were administered to 2,642 older adults with no impairment (n = 285), mild cognitive impairment (n = 1,057), and Alzheimer’s disease (n = 1,300). Predictive accuracy on functional …impairment level with the linear models of the single total score or multiple subtest scores (Model 1, 2) and support vector regression with low or high complexity (Model 3, 4) were compared across different sample sizes. Results: The linear models (Model 1, 2) showed superior performance with relatively smaller sample size, while nonlinear models with low and high complexity (Model 3, 4) showed an improved accuracy with a larger dataset. Unlike linear models, the nonlinear models showed a gradual increase in the predictive accuracy with a larger sample size (n > 500), especially when the model training is allowed to exploit complex patterns of the dataset. Conclusion: Our finding suggests that nonlinear models can predict levels of functional impairment with a sufficient dataset. The summary index of the neuropsychological battery can be augmented for specific purposes, especially in estimating the functional status of dementia. Show more
Keywords: Dementia, functional status, machine learning, neuropsychological tests
DOI: 10.3233/JAD-215244
Citation: Journal of Alzheimer's Disease, vol. 85, no. 3, pp. 1357-1372, 2022