Description

Book Synopsis

.- Syndrome Differentiation Thought in Traditional Chinese Medicine.
.- Overview of the evaluation task for syndrome differentiation thought in traditional Chinese medicine in CHIP2024.
.- Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG.
.- A TCM Syndrome Differentiation Thinking Method Based on Chain of Thought and Knowledge Retrieval Augmentation.
.- Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine.
.- Iterative Retrieval Augmentation for Syndrome Differentiation via Large Language Models.
.- Lymphoma Information Extraction and Automatic Coding.
.- Benchmark for Lymphoma Information Extraction and Automated Coding.
.- Overview of the Lymphoma Information Extraction and Automatic Coding Evaluation Task in CHIP 2024.
.- Automatic ICD Code Generation for Lymphoma Using Large Language Models.
.- Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-based Methods.
.- Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases.
.- Harnessing Retrieval-Augmented LLMs for Training-Free Tumor Coding Classification.
.- Hierarchical Information Extraction and Classification of Lymphoma Tumor Codes Based On LLM.
.- Typical Case Diagnosis Consistenc.
.- Benchmark of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.
.- Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.
.- The Diagnosis of Typical Medical Cases through Optimized Fine-Tuning of Large Language Models.
.- Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases.
.- Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework.
.- Typical Medical Case Diagnosis with Voting and Answer Discrimination using Fine-tuned LLM.
.- Reliable Typical Case Diagnosis via Optimized Retrieval-Augmented Generation Techniques.

Health Information Processing. Evaluation Track Papers

    Product form

    A Paperback by Yanchun Zhang

    £56.99

    Includes FREE delivery

    RRP £5,999.00 – you save £5,942.01 (99%)

    Order before 4pm tomorrow for delivery by Sat 10 Oct 2026.

    1 in stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Health Information Processing. Evaluation Track Papers by Yanchun Zhang

      Book details

      Publisher Springer
      Published 4/20/2025
      ISBN-13 9789819642977
      978-9819642977
      ISBN-10 9819642973
      Also in:
      Medical equipment, devices and techniques

      Description

      Book Synopsis

      .- Syndrome Differentiation Thought in Traditional Chinese Medicine.
      .- Overview of the evaluation task for syndrome differentiation thought in traditional Chinese medicine in CHIP2024.
      .- Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG.
      .- A TCM Syndrome Differentiation Thinking Method Based on Chain of Thought and Knowledge Retrieval Augmentation.
      .- Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine.
      .- Iterative Retrieval Augmentation for Syndrome Differentiation via Large Language Models.
      .- Lymphoma Information Extraction and Automatic Coding.
      .- Benchmark for Lymphoma Information Extraction and Automated Coding.
      .- Overview of the Lymphoma Information Extraction and Automatic Coding Evaluation Task in CHIP 2024.
      .- Automatic ICD Code Generation for Lymphoma Using Large Language Models.
      .- Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-based Methods.
      .- Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases.
      .- Harnessing Retrieval-Augmented LLMs for Training-Free Tumor Coding Classification.
      .- Hierarchical Information Extraction and Classification of Lymphoma Tumor Codes Based On LLM.
      .- Typical Case Diagnosis Consistenc.
      .- Benchmark of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.
      .- Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.
      .- The Diagnosis of Typical Medical Cases through Optimized Fine-Tuning of Large Language Models.
      .- Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases.
      .- Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework.
      .- Typical Medical Case Diagnosis with Voting and Answer Discrimination using Fine-tuned LLM.
      .- Reliable Typical Case Diagnosis via Optimized Retrieval-Augmented Generation Techniques.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account