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.

现代数据科学的核心要求是对数据和数据集进行注释以支持链接， 跨领域特定知识库的间接引用和推理。临床实验室数据必须是 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。 25岁以上 年，逻辑观察标识符名称和代码 (LOINC®) 术语标准 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 由组成部分逻辑地组成，以足够的细节来描述独特的概念以进行区分 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的推理。静态注释，同时建立独特的 生物医学概念的同一性，对于没有断言的推理和推论的目标没有贡献 a) 特定术语（例如 LOINC）内的概念之间的关系，最好是 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、与格式良好的外部本体的链接来实现， 演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 组件、术语和代码 转化为本体网络语言（OWL）渲染以支持推理。这将包括正式化 “超级代码”下的 LOINC 组和潜在相关聚合（例如所有血糖）。我们将链接 LOINC 组件 外部无阻碍本体的部件，例如生物的化学实体 利息（ChEBI）。这些链接可以告知 OWL 结构中声明的层次结构和关系。 我们将演示 OWL 和相关分层推理服务的应用，以允许集中、 直接或间接锚定在 LOINC 中的临床数据的拆分和链接。使用 FHIR 示例， 提供示例和代码库，允许查询和聚合观察结果（例如所有血液 葡萄糖）。 Reasoning LOINC 将作为开放获取资源进行分发，与 OBO 保持一致 社区和相关生物医学术语和分类资源。我们将利用现有团体 以及 LOINC 用户组、CD2H 和 ACT 等组织，以征求用例并动态评估 本体的发展和优先事项。