Natural language processing-based classification of early Alzheimer's disease from connected speech.

ABSTRACT:

Introduction

The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific for the detection of AD.

Methods

We applied a language model to automatically transcribed connected speech from 114 Flemish-speaking individuals to first distinguish early AD patients from amyloid negative cognitively unimpaired (CU) and then amyloid negative from amyloid positive CU individuals using five different types of connected speech.

Results

The language model was able to distinguish between amyloid negative CU subjects and AD patients with up to 81.9% sensitivity and 81.8% specificity. Discrimination between amyl

SUBMITTER: Balabin H 

PROVIDER: S-EPMC11848170 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature