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Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Min Ju | - |
dc.contributor.author | Kim, Sang Yun | - |
dc.contributor.author | Na, Duk L. | - |
dc.contributor.author | Kim, Byeong C. | - |
dc.contributor.author | Yang, Dong Won | - |
dc.contributor.author | Kim, Eun-Joo | - |
dc.contributor.author | Na, Hae Ri | - |
dc.contributor.author | Han, Hyun Jeong | - |
dc.contributor.author | Lee, Jae-Hong | - |
dc.contributor.author | Kim, Jong Hun | - |
dc.contributor.author | Park, Kee Hyung | - |
dc.contributor.author | Park, Kyung Won | - |
dc.contributor.author | Han, Seol-Heui | - |
dc.contributor.author | Kim, Seong Yoon | - |
dc.contributor.author | Yoon, Soo Jin | - |
dc.contributor.author | Yoon, Bora | - |
dc.contributor.author | Seo, Sang Won | - |
dc.contributor.author | Moon, So Young | - |
dc.contributor.author | Yang, YoungSoon | - |
dc.contributor.author | Shim, Yong S. | - |
dc.contributor.author | Baek, Min Jae | - |
dc.contributor.author | Jeong, Jee Hyang | - |
dc.contributor.author | Choi, Seong Hye | - |
dc.contributor.author | Youn, Young Chul | - |
dc.date.accessioned | 2020-03-20T08:19:24Z | - |
dc.date.available | 2020-03-20T17:20:35Z | - |
dc.date.issued | 2019-11-21 | - |
dc.identifier.citation | BMC Medical Informatics and Decision Making, 19(1):231 | ko_KR |
dc.identifier.issn | 1472-6947 | - |
dc.identifier.uri | 10.1186/s12911-019-0974-x | - |
dc.identifier.uri | https://hdl.handle.net/10371/164748 | - |
dc.description.abstract | Background
Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting. | ko_KR |
dc.description.sponsorship | The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from
the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BMC | ko_KR |
dc.subject | Machine learning | - |
dc.subject | Neuropsychological test | - |
dc.subject | Dementia | - |
dc.subject | Mild cognitive impairment | - |
dc.subject | Alzheimer’s disease | - |
dc.title | Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 강민주 | - |
dc.contributor.AlternativeAuthor | 김상윤 | - |
dc.contributor.AlternativeAuthor | 양동원 | - |
dc.contributor.AlternativeAuthor | 김은주 | - |
dc.contributor.AlternativeAuthor | 나해리 | - |
dc.contributor.AlternativeAuthor | 한현정 | - |
dc.contributor.AlternativeAuthor | 이재홍 | - |
dc.contributor.AlternativeAuthor | 김종훈 | - |
dc.contributor.AlternativeAuthor | 박기형 | - |
dc.contributor.AlternativeAuthor | 박경원 | - |
dc.contributor.AlternativeAuthor | 한설희 | - |
dc.contributor.AlternativeAuthor | 김성윤 | - |
dc.contributor.AlternativeAuthor | 윤수진 | - |
dc.contributor.AlternativeAuthor | 윤보라 | - |
dc.contributor.AlternativeAuthor | 서상원 | - |
dc.contributor.AlternativeAuthor | 문소영 | - |
dc.contributor.AlternativeAuthor | 양영순 | - |
dc.contributor.AlternativeAuthor | 백민재 | - |
dc.contributor.AlternativeAuthor | 정지향 | - |
dc.contributor.AlternativeAuthor | 최성혜 | - |
dc.contributor.AlternativeAuthor | 윤영철 | - |
dc.citation.journaltitle | BMC Medical Informatics and Decision Making | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2019-11-24T04:18:47Z | - |
dc.citation.number | 1 | ko_KR |
dc.citation.startpage | 231 | ko_KR |
dc.citation.volume | 19 | ko_KR |
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