迈向可扩展且跨语言的肿瘤学专用语言模型
Towards Scalable and Cross-Lingual Specialist Language Models for Oncology
AI总结：
本文提出一个肿瘤学专用、高效且可适应的NLP框架，结合指令微调、RAG和图知识整合，通过轻量级模型及少量德语指令实现跨语言迁移，在多个肿瘤学任务上取得优异表现。
摘要：
临床肿瘤学产生大量非结构化数据，这些数据往往包含不一致、信息缺失和歧义，使得为数据驱动决策提取可靠洞见变得困难。通用大语言模型（LLMs）由于缺乏领域特定的推理能力，包括专业临床术语、依赖上下文的解释以及多模态数据整合，难以应对这些挑战。我们通过一个肿瘤学专用、高效且可适应的自然语言处理框架来解决这些问题，该框架结合了指令微调、检索增强生成（RAG）和基于图的知识整合。我们的轻量级模型在肿瘤学特定任务上表现出有效性，例如命名实体识别（如识别癌症诊断）、实体链接（如将实体链接到标准化本体）、TNM分期、文档分类（如从病理报告中分类癌症亚型）以及治疗反应预测。我们的框架强调适应性和资源效率。我们纳入了在苏黎世大学医院（USZ）收集的少量德语指令，以测试少量非英语语言数据能否有效地跨语言迁移知识。这一方法反映了我们构建轻量级模型的动机，即在保持较强性能的同时降低计算成本，使其适用于资源有限的医疗环境。我们在肿瘤学数据集上验证了模型，在命名实体识别、关系抽取和文档分类方面展示了优异的结果。
英文摘要：
Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for data-driven decision-making. General-purpose large language models (LLMs) struggle with these challenges due to their lack of domain-specific reasoning, including specialized clinical terminology, context-dependent interpretations, and multi-modal data integration. We address these issues with an oncology-specialized, efficient, and adaptable NLP framework that combines instruction tuning, retrieval-augmented generation (RAG), and graph-based knowledge integration. Our lightweight models prove effective at oncology-specific tasks, such as named entity recognition (e.g., identifying cancer diagnoses), entity linking (e.g., linking entities to standardized ontologies), TNM staging, document classification (e.g., cancer subtype classification from pathology reports), and treatment response prediction. Our framework emphasizes adaptability and resource efficiency. We include minimal German instructions, collected at the University Hospital Zurich (USZ), to test whether small amounts of non-English language data can effectively transfer knowledge across languages. This approach mirrors our motivation for lightweight models, which balance strong performance with reduced computational costs, making them suitable for resource-limited healthcare settings. We validated our models on oncology datasets, demonstrating strong results in named entity recognition, relation extraction, and document classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
