Full Description
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.
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.
