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

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dc.contributor.authorKang, MJ-
dc.contributor.authorKim, SY-
dc.contributor.authorNa, DL-
dc.contributor.authorKim, BC-
dc.contributor.authorYang, DW-
dc.contributor.authorKim, EJ-
dc.contributor.authorNa, HR-
dc.contributor.authorHan, HJ-
dc.contributor.authorLee, JH-
dc.contributor.authorKim, JH-
dc.contributor.authorPark, KH-
dc.contributor.authorPark, KW-
dc.contributor.authorHan, SH-
dc.contributor.authorKim, SY-
dc.contributor.authorYoon, SJ-
dc.contributor.authorYoon, B-
dc.contributor.authorSeo, SW-
dc.contributor.authorMoon, SY-
dc.contributor.authorYang, Y-
dc.contributor.authorShim, YS-
dc.contributor.authorBaek, MJ-
dc.contributor.authorJeong, JH-
dc.contributor.authorChoi, SH-
dc.contributor.authorYoun, YC-
dc.description.abstractBACKGROUND: 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.
dc.subject.MESHAge Factors-
dc.subject.MESHAlzheimer Disease / diagnosis-
dc.subject.MESHCognitive Dysfunction / diagnosis-
dc.subject.MESHDatasets as Topic-
dc.subject.MESHDeep Learning-
dc.subject.MESHMachine Learning-
dc.subject.MESHNeural Networks, Computer-
dc.subject.MESHNeuropsychological Tests-
dc.subject.MESHSensitivity and Specificity-
dc.titlePrediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data-
dc.subject.keywordAlzheimer’s disease-
dc.subject.keywordMachine learning-
dc.subject.keywordMild cognitive impairment-
dc.subject.keywordNeuropsychological test-
dc.contributor.affiliatedAuthorMoon, SY-
dc.type.localJournal Papers-
dc.citation.titleBMC medical informatics and decision making-
dc.identifier.bibliographicCitationBMC medical informatics and decision making, 19(1). : 231-231, 2019-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Neurology
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