CAREER: Advancing the Role of Ontologies for Data Science in Biomedicine

本体是知识领域中概念(或类)、属性和概念之间关系的正式表示。本体论和术语在生物医学研究中发挥了至关重要的作用，用于编码、管理、共享和交换不断生成的海量异类生物医学数据，例如在电子健康记录(EHR)中。EHR已被广泛用于转译研究，以学习不同患者队列中发现和疾病管理的预测模型。这种基于电子病历的应用程序的第一步通常涉及患者队列识别。队列识别涉及一组资格标准的规范，需要使用电子病历的语义主干(即编码系统或本体)将其转换为可计算的表示，然后才能针对电子病历数据库运行查询。然而，要从大规模的EHR中进行有效的队列识别，存在两个关键障碍。第一个问题是数据(或语义)异构性，这是由于混合使用编码系统造成的。第二个是语义主干或本体层次的质量，这对于将患者资格标准转换为可执行的数据库查询至关重要。为了应对这些挑战，该项目将开发直接影响生物医学数据科学实践的本体匹配和本体质量提高的新方法，例如患者队列识别。此外，该项目将把拟议的计算方面纳入基于数据科学的课程，以培养下一代数据科学家。该项目由三个研究目标组成。在目标1中，PI将开发基于图形神经网络(GNN)的新学习方法，通过利用嵌入在统一医学语言系统等来源中的知识来匹配生物医学本体。这将解决异构性问题并实现语义互操作性。在目标2中，PI将开发基于学习的方法来检测子类关系中的质量缺陷。这将解决质量问题，并继续加强本体层次结构。在目标3中，PI将开发一个基于本体的新冠肺炎查询引擎，用于患者队列识别，这是一个增强语义互操作性的现实应用，以支持数据驱动的新冠肺炎研究。对于建议的方法的评估，领域专家将参与验证结果匹配的概念和检测到的质量问题。PI将向各自的本体所有者传达有效的质量问题，以便在后续的本体版本中进行更正。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

An ontology is a formal representation of concepts (or classes), properties, and relationships between concepts within a knowledge domain. Ontologies and terminologies have played a vital role in biomedical research for coding, managing, sharing, and exchange of vast amounts of heterogeneous biomedical data that are being continuously generated, such as in Electronic Health Records (EHRs). EHRs have been widely used in translational research to learn predictive models for discovery and disease management across varying patient cohorts. The very first step in such EHR-based applications often concerns patient cohort identification. Cohort identification involves the specification of a collection of eligibility criterion that needs to be transformed into a computable representation using the EHR’s semantic backbone (i.e., coding systems or ontologies) before queries can run against the EHR database. However, there are two critical barriers in performing effective cohort identification from large-scale EHRs. The first one is data (or semantic) heterogeneity, caused by a mixed utilization of coding systems. The second one is the quality of the semantic backbone or ontology hierarchy, which is essential for translating patient eligibility criteria to executable database queries. To address such challenges, this project will develop new methods for ontology matching and for ontology quality enhancement that directly impact data science practice in biomedicine, such as patient cohort identification. In addition, this project will incorporate the proposed computational aspects into data science-based courses to train next generation data scientists.This project consists of three research objectives. In Objective 1, the PI will develop new graph neural network (GNN)-based learning methods for matching biomedical ontologies by harnessing knowledge embedded in sources such as the Unified Medical Language System. This will address the heterogeneity issue and achieve semantic interoperability. In Objective 2, the PI will develop learning-based methods for detecting quality defects in subclass relations. This will address the quality issue and achieve continued enhancement of ontology hierarchies. In Objective 3, the PI will develop an ontology-based COVID-19 query engine for patient cohort identification, which is a real-world application of enhancing semantic interoperability for supporting data-driven COVID-19 research. For evaluation of the proposed methods, domain experts will be involved in validation of the resulted matching concepts and detected quality issues. The PI will communicate validated quality issues to the respective ontology owners for correction in subsequent ontology versions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.