Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

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 accurac...

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Main Authors: Min Ju Kang, Sang Yun Kim, Duk L. Na, Byeong C. Kim, Dong Won Yang, Eun-Joo Kim, Hae Ri Na, Hyun Jeong Han, Jae-Hong Lee, Jong Hun Kim, Kee Hyung Park, Kyung Won Park, Seol-Heui Han, Seong Yoon Kim, Soo Jin Yoon, Bora Yoon, Sang Won Seo, So Young Moon, YoungSoon Yang, Yong S. Shim, Min Jae Baek, Jee Hyang Jeong, Seong Hye Choi, Young Chul Youn
Format: Article
Language:English
Published: BMC 2019-11-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Neuropsychological test
Dementia
Mild cognitive impairment
Alzheimer’s disease
Online Access:http://link.springer.com/article/10.1186/s12911-019-0974-x

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http://link.springer.com/article/10.1186/s12911-019-0974-x

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