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Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts.

Ziming Liu1, Eun Jin Paek2, Si On Yoon3

  • 1Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA.

Journal of Alzheimer'S Disease : JAD
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

Referential communication tasks (RCTs) show promise for diagnosing Alzheimer's disease (AD). Natural language processing (NLP) models accurately distinguish AD patients from healthy adults using speech analysis from these tasks.

Keywords:
Alzheimer’s diseaseearly diagnosisnatural language processingtransfer learning

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Area of Science:

  • Computational linguistics
  • Neuroscience
  • Artificial intelligence in healthcare

Background:

  • Alzheimer's disease (AD) impairs discourse production.
  • Referential communication tasks (RCTs) assess object description abilities in conversation.

Purpose of the Study:

  • Evaluate RCTs for distinguishing Alzheimer's disease (AD) from healthy cognition.
  • Utilize Natural Language Processing (NLP) for speech analysis in RCTs.

Main Methods:

  • Applied machine learning to transcribed RCT speech from 28 older adults (12 with AD).
  • Compared classification using linguistic features versus NLP transfer learning (BERT).

Main Results:

  • A designed NLP transfer learning algorithm achieved superior Alzheimer's disease (AD) detection.
  • High accuracy in AD detection was observed even with transcripts from a single image.

Conclusions:

  • RCTs show potential as a practical diagnostic tool for Alzheimer's disease (AD).
  • Simplified RCTs (fewer images) maintain diagnostic accuracy.
  • RCTs can enhance understanding of discourse deficits in Alzheimer's disease (AD).