用思维链与检索增强结合，提升大模型从病历中诊断罕见病的能力。
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes
- 设计双路径框架：先推理后检索，或先检索后推理，模拟专家诊断流程。
- 在5980份病历上，使用DeepSeek模型的方案顶10基因准确率超40%。
- 适合临床科研人员和医学AI开发者参考，尤其处理杂乱病历场景。
背景：大型语言模型（LLMs）在基于表型的罕见病致病基因优先排序任务中表现不佳。以往研究多用人类表型本体（HPO）术语提示GPT、LLaMA等基础模型预测候选基因，但真实临床中输入为非结构化病历文本，且基础模型未针对特定领域优化。如何指导模型从非结构化病历中推断候选基因或疾病诊断仍是重大挑战。方法：提出RAG驱动的CoT与CoT驱动的RAG两种方法，将思维链（CoT）与检索增强生成（RAG）结合分析临床病历。采用五步思维链模拟专家推理过程，同时通过RAG从HPO、OMIM等数据库中检索信息。在包含5,980份基于Phenopacket的病历、255篇文献叙述及220份费城儿童医院内部病历的数据集上进行评估。结果：最新基础模型如Llama 3.3-70B-Instruct和DeepSeek-R1-Distill-Llama-70B优于早期版本（如Llama 2、GPT-3.5）。RAG驱动的CoT与CoT驱动的RAG均显著优于基础模型；其中，以DeepSeek为底座的方法在Phenopacket病历上实现超过40%的顶10基因准确率。当病历质量高时，先检索再推理更优，可锚定后续推理于领域证据；而对长篇且嘈杂的病历，先推理再检索更具优势。
原文摘要 · Abstract (English)
Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Childrens Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes.
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