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

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    A Paperback by Yanchun Zhang

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      Publisher: Springer
      Publication Date: 4/20/2025
      ISBN13: 9789819642977, 978-9819642977
      ISBN10: 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.

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