构建可验证的癌症知识图谱，辅助结直肠癌诊疗决策。
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care
- 用无监督方法自动整合最新医学文献，构建动态更新的知识图谱。
- 结合大模型与知识图谱，减少幻觉并提升检索准确性。
- 提供五种交互界面，适配不同临床与科研使用场景。
本文介绍了一个面向结直肠癌的全球规模、可交互、可验证的知识图谱-大语言模型混合系统，整合了最新的同行评审医学知识。该系统正在美国顶尖癌症中心之一的莫菲特癌症中心用于辅助医学研究和临床信息检索。相比单独使用大模型、知识图谱或搜索引擎，该混合系统更能满足用户需求。大模型存在幻觉和灾难性遗忘问题，且训练数据常过时；而现有先进知识图谱如PrimeKG、cBioPortal、ChEMBL、NCBI等需人工维护，迅速过时。CancerKG采用无监督方式，可自动摄入并组织最新医学发现。为缓解大模型缺陷，经验证的知识图谱作为检索增强生成（RAG）的约束机制。系统提供五种先进用户界面，分别优化不同数据模态的使用体验，更加便捷高效。
原文摘要 · Abstract (English)
Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center, which is one of the top Cancer centers in the U.S. and in the world. Our hybrid is remarkable as it serves the user needs better than just an LLM, KG or a search-engine in isolation. LLMs as is are known to exhibit hallucinations and catastrophic forgetting as well as are trained on outdated corpora. The state of the art KGs, such as PrimeKG, cBioPortal, ChEMBL, NCBI, and other require manual curation, hence are quickly getting stale. CancerKG is unsupervised and is capable of automatically ingesting and organizing the latest medical findings. To alleviate the LLMs shortcomings, the verified KG serves as a Retrieval Augmented Generation (RAG) guardrail. CancerKG exhibits 5 different advanced user interfaces, each tailored to serve different data modalities better and more convenient for the user.
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