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

arXiv: https://arxiv.org/abs/1511.02436 · HTML: https://mlsanity.com/paper/1511.02436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Alzheimer's Diagnosis and Generation-Based Chatbot Using Hierarchical Attention and Transformer](https://mlsanity.com/paper/2211.07703/) (2022)

## Abstract (arXiv)

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.