Health Information Processing. Evaluation Track Papers

10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 15–17, 2024, Proceedings

AvYanchun Zhang,Qingcai Chen

Häftad, Engelska, 2025

Del i serien Communications in Computer and Information Science

805 kr

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Beskrivning

mso-bidi-theme-font: minor-latin;">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;

Produktinformation

Utgivningsdatum:2025-04-13
Mått:155 x 235 x 14 mm
Vikt:382 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:228
Förlag:Springer Nature Switzerland AG
ISBN:9789819642977

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

.- 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.