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Related Experiment Video

Updated: Nov 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation.

Yanghui Li1, Guihua Wen1, Yang Hu1

  • 1School of Computer Science & Engineering, South China University of Technology, Guangzhou, China; Guangdong Engineering Technology Research Center for Artificial intelligence and traditional Chinese Medicine, Guangzhou, China.

Journal of Biomedical Informatics
|March 13, 2021
PubMed
Summary

This study introduces a knowledge-guided multi-source Seq2seq model for Chinese online healthcare consultations. The new method improves response quality by incorporating domain knowledge, enhancing diagnostic accuracy and treatment advice.

Keywords:
Attention mechanismDialog generationDomain knowledgeHealthcare consultationRecurrent neural network

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Online healthcare consultations offer convenience but face challenges with response quality in generative dialog systems.
  • Traditional Seq2seq models often produce low-quality or irrelevant responses for medical queries.
  • Existing multi-source Seq2seq architectures improve informativeness but can still generate inaccurate diagnostic or treatment advice.

Purpose of the Study:

  • To develop an improved generative dialog system for Chinese healthcare consultations.
  • To address the issue of inaccurate or inappropriate responses in existing multi-source Seq2seq models.
  • To enhance the quality and reliability of automated medical advice through knowledge integration.

Main Methods:

  • Proposed a novel multi-source Seq2seq guided by knowledge (MSSGK) model.
  • Integrated domain knowledge, including disease and topic labels, using a multi-task learning framework.
  • Introduced three attention mechanisms to guide response generation and improve knowledge exploitation.

Main Results:

  • Experimental results demonstrated the effectiveness of the proposed MSSGK method.
  • The model showed significant improvements in generating accurate and relevant responses for healthcare consultations.
  • The integration of domain knowledge and attention mechanisms enhanced the system's performance.

Conclusions:

  • The MSSGK model offers a promising approach for building high-quality generative dialog systems in healthcare.
  • Incorporating domain knowledge is crucial for improving the accuracy and appropriateness of AI-driven medical consultations.
  • The proposed attention mechanisms effectively guide the generation of more reliable medical advice.