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Predicting Mild Cognitive Impairment (MCI) is currently a challenge asexisting diagnostic criteria rely on neuropsychological examinations. AutomatedMachine Learning (ML) models that are trained on verbal utterances of MCIpatients can aid diagnosis. Using a combination of skip-gram features, ourmodel learned several linguistic biomarkers to distinguish between 19 patientswith MCI and 19 healthy control individuals from the DementiaBank languagetranscript clinical dataset. Results show that a model with compound ofskip-grams has better AUC and could help ML prediction on small MCI datasample.

Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams