Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams
This work addresses the challenge of automated MCI diagnosis for clinicians, but it is incremental as it builds on existing skip-gram methods with a small dataset.
The paper tackled predicting Mild Cognitive Impairment (MCI) by developing a machine learning model using compound skip-gram features on language transcripts from 19 MCI patients and 19 healthy controls, achieving better AUC results.
Predicting Mild Cognitive Impairment (MCI) is currently a challenge as existing diagnostic criteria rely on neuropsychological examinations. Automated Machine Learning (ML) models that are trained on verbal utterances of MCI patients can aid diagnosis. Using a combination of skip-gram features, our model learned several linguistic biomarkers to distinguish between 19 patients with MCI and 19 healthy control individuals from the DementiaBank language transcript clinical dataset. Results show that a model with compound of skip-grams has better AUC and could help ML prediction on small MCI data sample.