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Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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Diabetes: Management and Pharmacotherapy01:15

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
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Glucagon-like Receptor Agonists01:24

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Incretins include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which stimulate insulin secretion post-meals. In type 2 diabetes, GIP's efficacy is reduced, making GLP-1 a viable drug target. GIP originates from preproGIP.
GLP-1, when administered in high doses intravenously, triggers insulin secretion, inhibits glucagon release, slows gastric emptying, reduces food intake, and restores normal insulin secretion. However, its rapid inactivation by...
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Related Experiment Video

Updated: May 8, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Enhancing Large Language Model Reliability: Minimizing Hallucinations with Dual Retrieval-Augmented Generation Based

Jaedong Lee1,2, Hyosoung Cha1, Yul Hwangbo1,2

  • 1Healthcare AI Team, National Cancer Center, Goyang-si 10408, Gyeonggi-do, Republic of Korea.

Journal of Personalized Medicine
|December 27, 2024
PubMed
Summary

This study developed a dual retrieval-augmented generation (RAG) system to improve large language model (LLM) accuracy in diabetes management. The novel system enhances AI reliability for current medical information across different languages and guidelines.

Keywords:
diabetes managementensemble retrieverlarge language modelsmedical information retrievalretrieval-augmented generation

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Informatics
  • Natural Language Processing

Background:

  • Large language models (LLMs) show potential in healthcare but struggle with accuracy (hallucinations), especially in dynamic fields like diabetes management.
  • Existing LLM updating methods are resource-intensive, creating a need for efficient ways to provide current medical information.
  • Ensuring the reliability and up-to-dateness of AI-generated medical content is critical for safe clinical application.

Purpose of the Study:

  • To develop and evaluate a novel retrieval system to enhance the reliability of large language models (LLMs) in diabetes management.
  • To create a dual retrieval-augmented generation (RAG) system capable of integrating and utilizing information from diverse international guidelines.
  • To assess the system's performance across different languages and identify optimal retrieval strategies for improved accuracy.

Main Methods:

  • A dual retrieval-augmented generation (RAG) system was developed, integrating the Korean Diabetes Association and American Diabetes Association 2023 guidelines.
  • The system utilized dense retrieval with 11 embedding models (including OpenAI, Upstage, and multilingual options) and sparse retrieval via the BM25 algorithm with language-specific tokenizers.
  • Performance was evaluated using various top-k values to optimize ensemble retrievers for each guideline, focusing on both Korean and English texts.

Main Results:

  • For dense retrieval, Upstage's Solar Embedding-1-large and OpenAI's text-embedding-3-large excelled for Korean and English, respectively; multilingual models outperformed language-specific ones.
  • The ko_kiwi tokenizer showed superior performance for Korean sparse retrieval, while ko_kiwi and porter_stemmer were comparable for English.
  • Optimized ensemble retrievers, combining dense and sparse methods, improved information coverage while maintaining precision.

Conclusions:

  • A dual RAG system effectively enhances LLM reliability for diabetes management information across languages.
  • The system's successful application with both Korean and American guidelines demonstrates its cross-regional utility.
  • This work provides a foundation for developing more trustworthy AI-assisted healthcare applications in diverse global contexts.