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The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...
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Related Experiment Video

Updated: May 29, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Classification of Chronic Dizziness Using Large Language Models.

Xiaowei Xu1,2, Ruixuan Jiang3,4,5, Si Zheng1,6

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100020 China.

Journal of Healthcare Informatics Research
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

DizzyInsight, a new model using large language models (LLM), improves chronic dizziness classification, especially for persistent postural-perceptual dizziness (PPPD) and anxiety disorders.

Keywords:
Anxiety and depressive disordersChronic dizzinessDecision supportEtiological classificationPersistent postural-perceptual dizziness (PPPD)

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Neurology

Background:

  • Accurate classification of chronic dizziness disorders, including persistent postural-perceptual dizziness (PPPD), anxiety, and depressive disorders, is challenging in primary care.
  • Existing diagnostic approaches may lack the precision needed for complex etiological classification of chronic dizziness.
  • Large language models (LLMs) offer potential for enhancing diagnostic accuracy in medicine.

Purpose of the Study:

  • To introduce DizzyInsight, an innovative etiological classification model for chronic dizziness.
  • To enhance the accuracy and reliability of LLM and machine learning (ML) approaches for classifying chronic dizziness.
  • To evaluate the performance of DizzyInsight in classifying specific chronic dizziness disorders.

Main Methods:

  • Eight expert physicians defined and evaluated chronic dizziness characteristics.
  • A dataset of 260 patients (105 males, 155 females, mean age 59.52 ± 13 years) with chronic dizziness was compiled.
  • The DizzyInsight model, leveraging LLM, was developed and compared against general LLM and BERT models for etiological classification.

Main Results:

  • DizzyInsight demonstrated a positive predictive value of 0.69 and sensitivity of 0.86 for persistent postural-perceptual dizziness (PPPD).
  • For anxiety and depressive disorders, DizzyInsight achieved a positive predictive value of 0.81 and sensitivity of 0.66.
  • The DizzyInsight model, using LLM, showed improved efficacy and interpretability in chronic dizziness etiological classification.

Conclusions:

  • DizzyInsight shows significant potential in leveraging LLM for accurate etiological classification of chronic dizziness disorders.
  • The model's performance suggests improved diagnostic capabilities for conditions like PPPD and anxiety/depressive disorders.
  • Further research is needed to refine evidence identification, assess primary care applicability, and validate external validity.