Computational LOINC to Support Biomedical Research at Scale

计算 LOINC 支持大规模生物医学研究

• 批准号: 10093337
• 负责人: CHRISTOPHER G CHUTE
• 金额: $ 32.93万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10093337
• 关键词: Adopted; Biological; Biomedical Research; Blood Glucose; Catalogs; Chemicals; Classification; Clinical; Clinical Data; Code; Communities; Data; Data Collection; Data Science; Data Set; Databases; Diagnostic; Discrimination; Education; Electronic Health Record; Elements; Engineering; Fast Healthcare Interoperability Resources; Feedback; Genomics; Goals; Institutes; Laboratories; Libraries; Link; Logic; Logical Observation Identifiers Names and Codes; Maps; Metabolic; Modeling; Modernization; Ontology; Play; Questionnaires; Reference Standards; Report (document); Research; Resources; Role; Services; Source; Structure; Terminology; Trust; Web Ontology Language; Work; base; biomedical data science; biomedical ontology; clinical practice; computational reasoning; data science resource; interest; interoperability; knowledge base; ontology development; open data

A core requirement for modern data science is the annotation of data and datasets to support linkage, indirect reference, and reasoning across domain specific knowledgebases. Clinical laboratory data must be annotated with standard reference concepts to seamlessly play its part in data-science analytics. For over 25 years, the Logical Observation Identifiers Names and Codes (LOINC®) terminology standard from the Regenstrief Institute has played the role of trusted identifiers for many clinical observations. LOINC codes are logically composed from constituent Parts to describe unique concepts with sufficient detail to discriminate specific labs and clinical findings. However, data science ultimately seeks to apply computational reasoning and inferencing across data collections and public datasets. Static annotations, while establishing unique identities for biomedical concepts, do not contribute to the goals of reasoning and inference absent asserted relationships between and among a) the concepts within a specific terminology such as LOINC, and ideally b) concepts in related terminologies and ontologies. The core purpose of this proposal is to engineer LOINC content so that datasets that are annotated with LOINC elements (codes and concepts) will facilitate data science analytics. This will be achieved through OWL rendering, linkage to well-formed external ontologies, demonstrating applications that leverage the logical associations, and engaging the LOINC and data science communities to prioritize and validate these efforts. We will restructure LOINC components, terms, and codes into an Ontology Web Language (OWL) rendering to support reasoning. This will include the formalization of LOINC groups and potential related aggregations under “uber codes” (e.g. all blood glucoses). We will link LOINC Components Parts to external, unencumbered ontologies such as Chemical Entities of Biological Interest (ChEBI). These linkages can inform the hierarchy and relationships asserted in the OWL structure. We will demonstrate the application of OWL and related hierarchical reasoning services to allow lumping, splitting and linking of clinical data that is directly or indirectly anchored in LOINC. Using FHIR examples, provide examples and code libraries that allow observations to be queried and aggregated (e.g. all blood glucoses). Reasoning LOINC will be distributed as an open-access resource, in harmony with the OBO community and related biomedical terminology and classification resources. We will leverage existing groups and organizations such as LOINC Users group, CD2H, and ACT, to solicit use cases and dynamically evaluate ontology development and priorities.

现代数据科学的一个核心要求是对数据和数据集进行注释以支持链接， 间接引用和跨领域特定知识库的推理。临床实验室数据必须 使用标准参考概念进行注释，以无缝地在数据科学分析中发挥作用。超过25 多年来，逻辑观察标识符名称和代码（LOINC®）术语标准来自 Regenstrief Institute在许多临床观察中发挥了可信标识符的作用。LOINC代码是 由组成部分逻辑组成，以足够的细节描述独特的概念，以区分 具体的实验室和临床结果然而，数据科学最终寻求应用计算推理 以及跨数据集合和公共数据集进行推理。静态注释，同时建立唯一的 身份的生物医学概念，不有助于目标的推理和推理缺席断言 a）特定术语（如LOINC）中的概念与理想情况下B） 相关术语和本体中的概念。该提案的核心目的是设计LOINC 内容，以便使用LOINC元素（代码和概念）注释的数据集将有助于数据 科学分析这将通过OWL渲染，连接到格式良好的外部本体， 展示利用逻辑关联的应用程序，并参与LOINC和数据科学 社区优先考虑并验证这些努力。我们将重组LOINC的组成部分，条款和代码 转化为本体Web语言（OWL）渲染，以支持推理。这将包括正式确定 LOINC组和“uber代码”下的潜在相关聚集（例如，所有血糖）。我们将链接 LOINC组件部分到外部的，不受阻碍的本体论，如化学实体或生物实体 利息（ChEBI）。这些联系可以告知OWL结构中断言的层次结构和关系。 我们将展示OWL和相关的分层推理服务的应用，以允许集总， 拆分和链接直接或间接锚定在LOINC中的临床数据。以FHIR为例， 提供允许查询和汇总观察结果的示例和代码库（例如，所有血液 葡萄糖）。推理LOINC将作为一个开放获取的资源分发，与OBO协调一致 社区和相关的生物医学术语和分类资源。我们将利用现有的团体 以及LOINC用户组、CD 2 H和ACT等组织，以征集用例并动态评估 本体论的发展和优先事项。