An explainable machine learning model of cognitive decline derived from speech.

ABSTRACT:

Introduction

Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders.

Methods

Speech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created.

Results

This approach was 75% accurate overall-an improvement over tradit

SUBMITTER: Chandler C 

PROVIDER: S-EPMC10752754 | biostudies-literature | 2023 Oct-Dec

REPOSITORIES: biostudies-literature