2015/11/07 by Sylvester Olubolu Orimaye, Kah Yee Tai, Orimaye, Sylvester Olubolu +6
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Dementia and Cognitive Impairment Research #FOS: Computer and information sciences #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1511.02436
Accepted and presented at the 2015 NIPS Workshop on Machine Learning in Healthcare (MLHC), Montreal, Canada
openalex publication_date 2015/11/07 · arxiv created 2015/12/10 · arxiv updated 2015/12/11 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
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.