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.
@article{arxiv.1511.02436,
title = {Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams},
author = {Sylvester Olubolu Orimaye and Kah Yee Tai and Jojo Sze-Meng Wong and Chee Piau Wong},
journal= {arXiv preprint arXiv:1511.02436},
year = {2015}
}
Comments
Accepted and presented at the 2015 NIPS Workshop on Machine Learning in Healthcare (MLHC), Montreal, Canada