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

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Authors
Kang, Min Ju; Kim, Sang Yun; Na, Duk L.; Kim, Byeong C.; Yang, Dong Won; Kim, Eun-Joo; Na, Hae Ri; Han, Hyun Jeong; Lee, Jae-Hong; Kim, Jong Hun; Park, Kee Hyung; Park, Kyung Won; Han, Seol-Heui; Kim, Seong Yoon; Yoon, Soo Jin; Yoon, Bora; Seo, Sang Won; Moon, So Young; Yang, YoungSoon; Shim, Yong S.; Baek, Min Jae; Jeong, Jee Hyang; Choi, Seong Hye; Youn, Young Chul
Issue Date
2019-11-21
Publisher
BMC
Citation
BMC Medical Informatics and Decision Making, 19(1):231
Keywords
Machine learningNeuropsychological testDementiaMild cognitive impairmentAlzheimer’s disease
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 Alzheimer’s 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.
ISSN
1472-6947
Language
English
URI
https://doi.org/10.1186/s12911-019-0974-x

http://hdl.handle.net/10371/164748
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College of Medicine/School of Medicine (의과대학/대학원)Neurosurgery (신경외과학전공)Journal Papers (저널논문_신경외과학전공)
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