{"product_id":"health-information-processing-evaluation-track-papers-9789819642977","title":"Health Information Processing. Evaluation Track Papers","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Syndrome Differentiation Thought in Traditional Chinese Medicine\u003c\/strong\u003e.\u003cbr\u003e.- Overview of the evaluation task for syndrome differentiation thought in traditional Chinese medicine in CHIP2024.\u003cbr\u003e.- Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG.\u003cbr\u003e.- A TCM Syndrome Differentiation Thinking Method Based on Chain of Thought and Knowledge Retrieval Augmentation.\u003cbr\u003e.- Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine.\u003cbr\u003e.- Iterative Retrieval Augmentation for Syndrome Differentiation via Large Language Models.\u003cbr\u003e\u003cstrong\u003e.- Lymphoma Information Extraction and Automatic Coding.\u003c\/strong\u003e\u003cbr\u003e.- Benchmark for Lymphoma Information Extraction and Automated Coding.\u003cbr\u003e.- Overview of the Lymphoma Information Extraction and Automatic Coding Evaluation Task in CHIP 2024.\u003cbr\u003e.- Automatic ICD Code Generation for Lymphoma Using Large Language Models.\u003cbr\u003e.- Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-based Methods.\u003cbr\u003e.- Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases.\u003cbr\u003e.- Harnessing Retrieval-Augmented LLMs for Training-Free Tumor Coding Classification.\u003cbr\u003e.- Hierarchical Information Extraction and Classification of Lymphoma Tumor Codes Based On LLM.\u003cbr\u003e\u003cstrong\u003e.- Typical Case Diagnosis Consistenc.\u003c\/strong\u003e\u003cbr\u003e.- Benchmark of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.\u003cbr\u003e.- Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024.\u003cbr\u003e.- The Diagnosis of Typical Medical Cases through Optimized Fine-Tuning of Large Language Models.\u003cbr\u003e.- Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases.\u003cbr\u003e.- Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework.\u003cbr\u003e.- Typical Medical Case Diagnosis with Voting and Answer Discrimination using Fine-tuned LLM.\u003cbr\u003e.- Reliable Typical Case Diagnosis via Optimized Retrieval-Augmented Generation Techniques.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":52195608658263,"sku":"9789819642977","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819642977.jpg?v=1763648282","url":"https:\/\/bookcurl.com\/products\/health-information-processing-evaluation-track-papers-9789819642977","provider":"Book Curl","version":"1.0","type":"link"}