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Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study

Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study

Performance of features (by categories) for classification of deficits in baseline visit using top 10 features. a Incudes gender as a feature. b Combined: acoustic, psycholinguistic, and sociodemographic features. c Mo CA: Montreal Cognitive Assessment. d ANN: artificial neural network. e NB: naïve Bayes. f HVLT: Hopkins Verbal Learning Test. g Animals T-score (D-KEFS Norms). h FAS total T-score (D-KEFS Norms). i SVM: support vector machine.

Varsha D Badal, Jenna M Reinen, Elizabeth W Twamley, Ellen E Lee, Robert P Fellows, Erhan Bilal, Colin A Depp

JMIR Aging 2024;7:e54655

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets

estimating Montreal Cognitive Assessment (Mo CA) scores was trained on the US data set and tested on the Japan data set; (B) plot of the drawing speed variability with respect to the Mo CA score for the US and Japan data sets, in which each point represents 1 participant and the solid line represents the regression line for the combined data set; (C) plot of the estimated and actual Mo CA scores in the Japan data set, in which each point represents 1 participant and the solid line represents the regression line; (D)

Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Varsha D Badal, Danielle Glorioso, Ellen E Lee, Rebecca Daly, Camille Nebeker, Elizabeth W Twamley, Colin Depp, Miyuki Nemoto, Kiyotaka Nemoto, Ho-Cheol Kim, Tetsuaki Arai, Dilip V Jeste

JMIR Form Res 2022;6(5):e37014