背景:医学知识图谱提供了可解释的决策支持,帮助临床医生提供及时的诊断和治疗建议。然而,在现实世界的临床实践中,患者前往不同的医院寻求各种医疗服务,导致不同医院的患者数据分散。由于数据安全问题,数据碎片化限制了知识图的应用,因为单医院数据无法为生成精确的决策支持和全面的解释提供完整的证据。研究知识图谱系统多中心集成的新方法,信息敏感的医疗环境,使用零散的患者记录进行决策支持,同时保持数据隐私和安全性。
目的:本研究旨在提出一种面向电子健康记录(EHR)的知识图谱系统,用于与多中心零散的患者医疗数据进行协作推理,同时保护数据隐私。
方法:该研究引入了EHR知识图谱框架和新的协作推理过程,用于利用多中心碎片信息。该系统部署在每个医院中,并使用统一的语义结构和观察医疗结果伙伴关系(OMOP)词汇来标准化本地EHR数据集。该系统将本地EHR数据转换为语义格式并执行语义推理以生成中间推理结果。生成的中间发现使用hypernym概念来分离原始医疗数据。中间发现和哈希加密的患者身份通过区块链网络进行同步。多中心中间发现进行了最终推理和临床决策支持,而无需收集原始EHR数据。
结果:通过一项应用研究对该系统进行了评估,该研究涉及利用多中心片段化的EHR数据来提醒非肾脏病临床医生注意被忽略的慢性肾脏病(CKD)患者。该研究涵盖了3家医院的非肾病科1185名患者。患者至少访问了两家医院。其中,通过使用多中心EHR数据进行协作推理,确定124例患者符合CKD诊断标准,而单独来自个别医院的数据不能促进这些患者CKD的识别.临床医生的评估表明,78/91(86%)患者为CKD阳性。
结论:所提出的系统能够有效地利用多中心片段化的EHR数据进行临床应用。应用研究显示了该系统具有迅速和全面的决策支持的临床优势。
BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security.
OBJECTIVE: This
study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy.
METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP)
vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data.
RESULTS: The system underwent evaluation through an application
study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The
study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive.
CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application
study showed the clinical benefits of the system with prompt and comprehensive decision support.