Clinical classification of memory and cognitive impairment with multimodal digital biomarkers.

ABSTRACT:

Introduction

Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline.

Methods

To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment.

Results

Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics mode

SUBMITTER: Banks R 

PROVIDER: S-EPMC10884988 | biostudies-literature | 2024 Jan-Mar

REPOSITORIES: biostudies-literature