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数据和数据集的注释，以支持链接， 间接参考和跨领域特定知识库的推理。临床实验室数据必须是 用标准参考概念注释，无缝地在数据科学分析中发挥作用。超过25 多年，逻辑观察标识符名称和代码（LOCINC®）术语标准 Regenstrief Institute在许多临床观察中扮演了受信任的标识符的角色。 loinc代码是 从逻辑上从组成部分组成，以形容独特的概念，并有足够的细节来区分 特定的实验室和临床发现。但是，数据科学最终试图应用计算推理 以及跨数据收集和公共数据集的推断。静态注释，同时建立独特 生物医学概念的身份，没有助长推理和推断的目标 a）特定术语（例如Loinc）和理想情况b）中的概念之间的关系 相关术语和本体论的概念。该提案的核心目的是设计Loinc 内容以使带有liinc元素（代码和概念）注释的数据集将有助于数据 科学分析。这将通过猫头鹰渲染，与形成良好的外部本体论的联系来实现， 展示利用逻辑关联并参与LOINC和数据科学的应用程序 社区优先考虑和验证这些努力。我们将恢复LIENC组件，术语和代码 进入本体网络语言（OWL）渲染以支持推理。这将包括 在“ Uber代码”下（例如，所有血糖）下的LICER组和潜在的相关聚集。我们将链接 LICINC组件的零件零件置于外部，不受限制的本体论，例如生物学的化学实体 兴趣（Chebi）。这些联系可以告知猫头鹰结构中主张的层次结构和关系。 我们将演示猫头鹰和相关层次推理服务的应用，以允许集结， 直接或间接锚定在LICINC的临床数据的分裂和链接。使用FHIR示例， 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将作为开放式资源分发，与OBO和谐相处 社区和相关的生物医学术语和分类资源。我们将利用现有小组 以及诸如LOINC用户组，CD2H和ACT之类的组织，以征求用例并动态评估 本体发展和优先事项。