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This book constitutes the refereed proceedings of the 10th China Health Information Processing Conference, CHIP 2024, held in Fuzhou, China, November 15 17, 2024.
The CHIP 2024 Evaluation Track proceedings include 19 full papers which were carefully reviewed and grouped into these topical sections: syndrome differentiation thought in Traditional Chinese Medicine; lymphoma information extraction and automatic coding; and typical case diagnosis consistency.
List of contents
.- 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.
