使用大语言模型进行真实世界医学知识的结构化提取以支持摘要与搜索
Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
AI总结：
提出基于大语言模型的患者知识图谱提取方法，映射现有本体以支持自然语言数据提取，并在3360万患者的EHR数据库中验证其对Dravet综合征搜索及无标签BPAN患者发现的有效性。
摘要：
知识图谱的创建与维护可以加速真实世界数据中的疾病发现与分析。虽然疾病本体有助于生物数据注释，但编码类别（SNOMED-CT、ICD10、CPT）可能无法捕捉患者病情的细微差别或罕见疾病。跨数据源的多种疾病定义使本体映射和疾病聚类变得复杂。我们提出使用大语言模型提取技术创建患者知识图谱，允许通过自然语言而非僵化的本体层次结构进行数据提取。我们的方法映射到现有本体（MeSH、SNOMED-CT、RxNORM、HPO）以锚定提取的实体。使用包含3360万患者的大型门诊EHR数据库，我们通过搜索Dravet综合征患者来演示该方法，该疾病于2020年10月获得ICD10认可。我们描述了患者特定知识图谱的构建和基于症状的患者搜索。使用确诊的Dravet综合征ICD10代码作为真实标签，我们采用基于LLM的实体提取在锚定本体中表征患者。随后我们将该方法应用于识别Beta-propeller蛋白相关神经变性（BPAN）患者，展示了在缺乏真实标签情况下的真实世界发现。
英文摘要：
Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified categories (SNOMED-CT, ICD10, CPT) may not capture patient condition nuances or rare diseases. Multiple disease definitions across data sources complicate ontology mapping and disease clustering. We propose creating patient knowledge graphs using large language model extraction techniques, allowing data extraction via natural language rather than rigid ontological hierarchies. Our method maps to existing ontologies (MeSH, SNOMED-CT, RxNORM, HPO) to ground extracted entities. Using a large ambulatory care EHR database with 33.6M patients, we demonstrate our method through the patient search for Dravet syndrome, which received ICD10 recognition in October 2020. We describe our construction of patient-specific knowledge graphs and symptom-based patient searches. Using confirmed Dravet syndrome ICD10 codes as ground truth, we employ LLM-based entity extraction to characterize patients in grounded ontologies. We then apply this method to identify Beta-propeller protein-associated neurodegeneration (BPAN) patients, demonstrating real-world discovery where no ground truth exists.
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。
