An explainable machine learning model of cognitive decline derived from speech
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2023•Wiley Online Library
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 …
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 …
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 traditional speech‐based screening tools and a unimodal language‐based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data.
DISCUSSION
This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies.
Highlights
- Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.
- Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.
- AI predictions of cognitive decline are more accurate than experts and standard tools.
- The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
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