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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.
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.
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Inhaltsangabe
.- 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.
.- 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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