| Record Type: |
Electronic resources
: Monograph/item
|
| Title/Author: |
Health information processing/ edited by Yanchun Zhang ... [et al.]. |
| Reminder of title: |
evaluation track papers : 10th China Health Information Processing Conference, CHIP 2024, Fuzhou, China, November 15-17, 2024 : proceedings / |
| remainder title: |
CHIP 2024 |
| other author: |
Zhang, Yanchun. |
| corporate name: |
CHIP (Conference) |
| Published: |
Singapore :Springer Nature Singapore : : 2025., |
| Description: |
xvii, 228 p. :ill. (some col.), digital ;24 cm. |
| [NT 15003449]: |
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. |
| Contained By: |
Springer Nature eBook |
| Subject: |
Medical informatics - Congresses. - |
| Online resource: |
https://doi.org/10.1007/978-981-96-4298-4 |
| ISBN: |
9789819642984 |