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Health Information Processing. Evaluation Track Papers: 10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 1517, 2024, Proceedings [Pehme köide]

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  • Formaat: Paperback / softback, 228 pages, kõrgus x laius: 235x155 mm, 43 Illustrations, color; 11 Illustrations, black and white; XVII, 228 p. 54 illus., 43 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2458
  • Ilmumisaeg: 13-Apr-2025
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 9819642973
  • ISBN-13: 9789819642977
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Health Information Processing. Evaluation Track Papers: 10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 1517, 2024, ProceedingsSuurem pilt
  • Formaat: Paperback / softback, 228 pages, kõrgus x laius: 235x155 mm, 43 Illustrations, color; 11 Illustrations, black and white; XVII, 228 p. 54 illus., 43 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 2458
  • Ilmumisaeg: 13-Apr-2025
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 9819642973
  • ISBN-13: 9789819642977
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789819642977.html
Märksõnad:

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.

.- 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.