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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A Joint LLM-KG System for Disease Q&A.

Prakash C Sukhwal, Vaibhav Rajan, Atreyi Kankanhalli

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary

    This study introduces Disease Guru-Long-Form Question Answer (DG-LFQA), an automated system for answering lay users' health questions. DG-LFQA combines large language models (LLMs) and knowledge graphs (KGs) for more accurate and comprehensive medical information retrieval.

    Area of Science:

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

    Background:

    • Medical question answering (QA) assistants help lay users navigate health information, combating misinformation and complexity.
    • Current QA systems often use large language models (LLMs) or knowledge graphs (KGs), each with limitations in accuracy or scope.
    • Existing approaches struggle with automation, performance, and providing comprehensive, accurate answers to complex health queries.

    Purpose of the Study:

    • To develop a novel, automated disease QA system (DG-LFQA) for lay users.
    • To enhance the accuracy and completeness of answers to disease-related questions.
    • To address limitations of existing LLM- and KG-based QA systems through a joint reasoning approach.

    Main Methods:

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  • Designed Disease Guru-Long-Form Question Answer (DG-LFQA), an automated system for disease-related queries.
  • Utilized a joint reasoning approach combining large language models (LLMs) and knowledge graphs (KGs).
  • Focused on answering long-form questions relevant to lay users' health information needs.

    • Main Results:

    • The DG-LFQA system demonstrated improved efficacy compared to baseline systems.
    • Evaluation using various quality metrics confirmed the system's performance.
    • The joint reasoning approach effectively integrated LLM and KG capabilities for superior QA.

    Conclusions:

    • The developed DG-LFQA system offers an effective solution for automated, accurate, and comprehensive medical question answering for lay users.
    • Combining LLMs and KGs through joint reasoning overcomes limitations of individual approaches.
    • This advancement can reduce healthcare professional burden and improve public access to reliable health information.