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Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Hyun-Soo | - |
dc.contributor.author | Choe, Jin Yeong | - |
dc.contributor.author | Kim, Hanjoo | - |
dc.contributor.author | Han, Ji Won | - |
dc.contributor.author | Chi, Yeon Kyung | - |
dc.contributor.author | Kim, Kayoung | - |
dc.contributor.author | Hong, Jongwoo | - |
dc.contributor.author | Kim, Taehyun | - |
dc.contributor.author | Kim, Tae Hui | - |
dc.contributor.author | Yoon, Sungroh | - |
dc.contributor.author | Kim, Ki Woong | - |
dc.date.accessioned | 2019-01-17T01:15:56Z | - |
dc.date.available | 2019-01-17T10:17:05Z | - |
dc.date.issued | 2018-10-03 | - |
dc.identifier.citation | BMC Geriatrics, 18(1):234 | ko_KR |
dc.identifier.issn | 1471-2318 | - |
dc.identifier.uri | https://hdl.handle.net/10371/145160 | - |
dc.description.abstract | Background
The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abundance of information. To achieve low-cost high-accuracy diagnose performance for dementia using a neuropsychological battery, a novel framework is proposed using the response profiles of 2666 cognitively normal elderly individuals and 435 dementia patients who have participated in the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD). Methods The key idea of the proposed framework is to propose a cost-effective and precise two-stage classification procedure that employed Mini Mental Status Examination (MMSE) as a screening test and the KLOSCAD Neuropsychological Assessment Battery as a diagnostic test using deep learning. In addition, an evaluation procedure of redundant variables is introduced to prevent performance degradation. A missing data imputation method is also presented to increase the robustness by recovering information loss. The proposed deep neural networks (DNNs) architecture for the classification is validated through rigorous evaluation in comparison with various classifiers. Results The k-nearest-neighbor imputation has been induced according to the proposed framework, and the proposed DNNs for two stage classification show the best accuracy compared to the other classifiers. Also, 49 redundant variables were removed, which improved diagnostic performance and suggested the potential of simplifying the assessment. Using this two-stage framework, we could get 8.06% higher diagnostic accuracy of dementia than MMSE alone and 64.13% less cost than KLOSCAD-N alone. Conclusion The proposed framework could be applied to general dementia early detection programs to improve robustness, preciseness, and cost-effectiveness. | ko_KR |
dc.description.sponsorship | This study was supported by the Seoul National University Bundang Hospital (SNUBH) Research Fund [no. 12-2013-002], the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) [no. 2018R1A2B3001628], the Korean Health Technology R&D Project, Ministry for Health, Welfare, Family Affairs, the Republic of Korea [no. HI09C1379 (A092077)], the Creative Industrial Technology Development Program funded by the Ministry of Trade, Industry&Energy (MOTIE, Korea) [no. 10053249], Samsung Research Funding Center of Samsung Electronics under Project [no. SRFC-IT1601-05], and SNU ECE Brain Korea 21+ project in 2018. | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Neuropsychological tests | ko_KR |
dc.subject | Alzheimer disease | ko_KR |
dc.subject | Dementia | ko_KR |
dc.subject | Data mining | ko_KR |
dc.subject | Deep learning | ko_KR |
dc.title | Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 최현수 | - |
dc.contributor.AlternativeAuthor | 채진영 | - |
dc.contributor.AlternativeAuthor | 김한주 | - |
dc.contributor.AlternativeAuthor | 한지원 | - |
dc.contributor.AlternativeAuthor | 지연경 | - |
dc.contributor.AlternativeAuthor | 김가영 | - |
dc.contributor.AlternativeAuthor | 홍종우 | - |
dc.contributor.AlternativeAuthor | 김태현 | - |
dc.contributor.AlternativeAuthor | 김태희 | - |
dc.contributor.AlternativeAuthor | 윤성로 | - |
dc.contributor.AlternativeAuthor | 김기웅 | - |
dc.identifier.doi | 10.1186/s12877-018-0915-z | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2018-10-07T03:20:33Z | - |
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