Predicting Early Indicators of Cognitive Decline from Verbal Utterances

TL;DR

This study explores the use of linguistic features from verbal utterances to distinguish between control elderly, MCI, and AD patients, demonstrating machine learning's potential in early dementia diagnosis with limited data.

Contribution

It introduces a novel approach combining psycholinguistic and contextual language features to classify four dementia-related groups using imbalanced datasets.

Findings

Support Vector Machine with combined features improves classification accuracy.

First work to identify four clinical groups in a highly imbalanced dataset.

Linguistic biomarkers can assist in early and differential diagnosis of dementia.

Abstract

Dementia is a group of irreversible, chronic, and progressive neurodegenerative disorders resulting in impaired memory, communication, and thought processes. In recent years, clinical research advances in brain aging have focused on the earliest clinically detectable stage of incipient dementia, commonly known as mild cognitive impairment (MCI). Currently, these disorders are diagnosed using a manual analysis of neuropsychological examinations. We measure the feasibility of using the linguistic characteristics of verbal utterances elicited during neuropsychological exams of elderly subjects to distinguish between elderly control groups, people with MCI, people diagnosed with possible Alzheimer's disease (AD), and probable AD. We investigated the performance of both theory-driven psycholinguistic features and data-driven contextual language embeddings in identifying different clinically diagnosed groups. Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD. This is the first work to identify four clinical diagnosis groups of dementia in a highly imbalanced dataset. Our work shows that machine learning algorithms built on contextual and psycholinguistic features can learn the linguistic biomarkers from verbal utterances and assist clinical diagnosis of different stages and types of dementia, even with limited data.