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Related Concept Videos

Dementia01:30

Dementia

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Semantic Feature Extraction Using SBERT for Dementia Detection.

Yamanki Santander-Cruz1, Sebastián Salazar-Colores2, Wilfrido Jacobo Paredes-García1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Queretaro C.P. 76010, Mexico.

Brain Sciences
|February 25, 2022
PubMed
Summary

This study introduces a new semantic feature approach for early dementia detection, outperforming existing methods. The technique uses sentence embeddings and machine learning to analyze speech, improving diagnostic accuracy for Alzheimer's disease.

Keywords:
NLP feature extractionSBERTdementiasemantic analysissyntax analysis

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Area of Science:

  • Computational Linguistics
  • Neuroscience
  • Artificial Intelligence

Background:

  • Dementia is a progressive neurodegenerative disease impacting cognition, particularly in the elderly.
  • Early detection is crucial for managing dementia progression, as it is currently incurable.
  • Existing methods using syntactic linguistic features lack semantic depth for early diagnosis.

Purpose of the Study:

  • To develop a novel methodology for early dementia detection using semantic features.
  • To integrate sentence embeddings from Siamese BERT networks (SBERT) with various machine learning classifiers.
  • To evaluate the performance of semantic features against syntactic-based and BERT-only approaches.

Main Methods:

  • Extracted 17 demographic, lexical, syntactic, and semantic features from 550 oral samples (DementiaBank Pitt Corpus).
  • Employed Siamese BERT networks (SBERT) for sentence embeddings.
  • Utilized Support Vector Machine (SVM), K-nearest neighbors (KNN), Random Forest, and Artificial Neural Network (ANN) classifiers.
  • Calculated mutual information score to assess feature relevance and dependence on the MMSE score.

Main Results:

  • The proposed semantic features approach achieved classification performance metrics including 77% accuracy, 80% precision, 80% recall, and 80% F1 score.
  • Demonstrated superior performance compared to syntax-based methods and the BERT approach using only linguistic features.
  • Mutual information scores confirmed the dependence of extracted features on the Mini-Mental State Examination (MMSE) score.

Conclusions:

  • The novel semantic features methodology shows significant promise for accurate and early dementia detection.
  • Integrating semantic information enhances diagnostic capabilities beyond traditional syntactic analysis.
  • This approach offers a sensitive, noninvasive tool for identifying early-stage Alzheimer's disease.