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  3. Predicting Dementia From Spontaneous Speech Using Large Language Models.
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  3. Predicting Dementia From Spontaneous Speech Using Large Language Models.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Predicting dementia from spontaneous speech using large language models.

Felix Agbavor1, Hualou Liang1

  • 1School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, United States of America.

PLOS Digital Health
|February 22, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models like GPT-3 can predict dementia from speech using text embeddings. This AI approach shows promise for early Alzheimer's disease diagnosis and cognitive assessment.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Language impairment is a key biomarker for neurodegenerative diseases like Alzheimer's disease (AD).
  • Artificial intelligence (AI) and natural language processing (NLP) are increasingly used for early AD prediction via speech.
  • Few studies have explored large language models (LLMs), such as GPT-3, for early dementia diagnosis.

Purpose of the Study:

  • To investigate the efficacy of GPT-3 in predicting dementia from spontaneous speech.
  • To utilize GPT-3's semantic knowledge for generating text embeddings from speech data.
  • To assess the potential of GPT-3 text embeddings for early Alzheimer's disease diagnosis.

Main Methods:

  • Transcribed spontaneous speech data was used to generate text embeddings via the GPT-3 model.
  • Text embeddings were analyzed to distinguish individuals with AD from healthy controls.
  • The ability of text embeddings to infer cognitive testing scores from speech data was evaluated.

    • Main Results:

    • GPT-3 text embeddings reliably distinguished individuals with Alzheimer's disease from healthy controls based solely on speech.
    • Text embeddings accurately inferred subjects' cognitive testing scores from speech data.
    • This approach outperformed conventional acoustic feature-based methods and competed with fine-tuned models.

    Conclusions:

    • GPT-3 based text embedding is a viable method for assessing Alzheimer's disease directly from speech.
    • This AI-driven technique holds significant potential for improving the early diagnosis of dementia.
    • Leveraging LLMs for speech analysis offers a novel pathway for cognitive health assessment.