OMOP-2-OPMI: Ontologization of OMOP CDM using OPMI to support clinical data interoperability and analysis Long Tran 1, and Yongqun He 1 1 University of Michigan, Ann Arbor, MI, USA Abstract The OMOP Common Data Model (CDM) has been widely used as an open community data standard in observational data integration and analysis. However, it still has its drawbacks including weak semantics and interoperability with other CDMs. In this study, we report our ontologization of the OMOP CDM elements and the semantic relations among the elements using the Ontology of Precision Medicine and Investigation (OPMI). A total of 165 terms from 15 OMOP CDM tables has been mapped to OPMI, with 46 terms newly generated with OPMI namespace and the other terms reported from OBO reference ontologies. An Omop2Opmi.owl file was also generated by extracting the OMOP CDM related terms and relations from OPMI. Three categories of use cases are reported, using the ontology-level OMOP CDM element standardization and data integration, adverse event (AE) modeling, and COVID-19 clinical data studies. Following the Ontology of Adverse Events (OAE) definition, we developed a generalizable OMOP-AE model that transforms the OMOP data to systematically define, identify, and analyze specific adverse events following some medical interventions that include Drug/Device Exposure and Procedure Occurrence in OMOP. Overall, OMOP-2-OPMI complements and empower OMOP CDM for enhanced clinical data standardization, sharing, interoperability, and analysis. Keywords 1 OMOP, Common Data Model, ontology, OPMI, adverse events, COVID-19. model used by the National COVID Cohort 1. Introduction Collaborative (N3C, https://ncats.nih.gov/n3c). As of May 2022, the N3C data enclave has stored the records of 14 million persons, including over The Observational Medical Outcomes 5 million COVID+ cases. Based on the N3C data Partnership (OMOP) Common Data Model use design, the COVID-19 clinical data (CDM) is an open community data standard that warehouse data dictionary used in N3C is based aims to allow for systematic analysis of disparate on OMOP CDM, and the other data formats need observational databases [1]. With the CDM, the to be aligned with the OMOP CDM in order to be data contained in those databases can be entered and used in the N3C data enclave. transformed into a common format with a Therefore, the OMOP CDM has clearly played a common representation. OMOP CDM has been significant role in the data standardization and widely used to support the standardization of integration. various electronic medical records (EMR) and Still the OMOP CDM has its own drawbacks administrative claims within and outside the [2, 3]. One drawback is its weak semantics in that United States. Billions of patient records have OMOP CDM does not provide robust semantic been standardized using OMOP CDM. Recently, relations among CDM elements. Basically, the OMOP CDM has become an established data OMOP CDM provides the schema structure of a ICBO 2022, September 25-28, 2022, Ann Arbor, USA EMAILs: longtr@umich.edu (A.1 ); yongqunh@med.umich.edu (A. 2). ORCID: 0000-0002-5735-7540 (A. 1); 0000-0001-9189-9661 (A. 2) 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org) ©️ CEUR Workshop ceur-ws.org ISSN 1613-0073 1 Proceedings standardized a relational database that includes used to support the Kidney Precision Medicine over 10 tables, which has an inherent weakness in Project [9, 10]. We have been using the OPMI to terms of representing the relations among terms model and represent the core OMOP CDM from different tables. As a result, the layout of elements and relations among the elements [3]. OMOP and how it is set up to document patients’ This manuscript reports our usage and conditions could lead to ambiguities, inaccurate extension of the OPMI to ontologize the OMOP representations and erroneous counting [2]. CDM elements and the relations among these Another drawback is that OMOP CDM does not elements, and how such OMOP-2-OPMI inherently provide systematic interoperability ontologization supports systematic clinical data with other CDMs such as National Patient- interoperability, sharing, and integration. Centered Clinical Research Network (PCORnet) [4] and Clinical Data Interchange Standards 2. Methods Consortium (CDISC) [5]. In the N3C data integration, the COVID-19 data formulated with 2.1. OMOP CDM resource other CDMs are required to be harmonized based used in the study on OMOP CDM version 5.3 [6], which is separately conducted and difficult to achieve The OMOP version 5.4 was used in our OPMI robust interoperability and scalability. mapping. First, we obtained terms and their Ontology can be a solution to solve the above annotations from the OMOP CDM version 5.4 drawbacks [3, 7]. In the 2018 OHDSI resource [12]. The Athena software program Symposium, we proposed a strategy of (https://athena.ohdsi.org/) is the tool used to ontological representation of the OMOP CDM search OMOP CDM terms and related terms from using the OBO framework [3]. In addition to the OMOP-associated terminologies. core OMOP CDM model, the OMOP system also includes many standardized clinical terminologies that can be used under the OMOP CDM 2.2. OMOP-2-OPMI framework to collaboratively support development strategy observational data standardization and integration. In the 2020 OHDSI Symposium, The OPMI ontology is used as the default Callahan et al. reports their development of the ontology platform for the ontology mapping and OMOP2OBO, a health system-wide program of new term generation of the OMOP CDM elements the integration and alignment between OMOP’s and semantic relations among the elements. In standardized clinical terminologies and eight general, the eXtensible Ontology Development OBO biomedical ontologies spanning diseases, (XOD) strategy [13], including the methods of phenotypes, anatomical entities, cell types, ontology term reuse, semantic alignment, organisms, chemicals, metabolites, hormones, ontology design pattern, and community vaccines, and proteins [7]. As of the end of May extensibility, were used for the OPMI mapping. 2022, the OMOP2OBO mapping program has Specifically, all those OMOP CDM element terms collected 92,367 OMOP Conditions, 8,615 Drug were first searched in Ontobee [14]. For those Exposure ingredients, and 3,827 Measurements terms existing in reference OBO ontologies that (10,673 measurement test results) terms [8]. map to the OMOP CDM elements, Ontofox [15] OMOP2OBO allows its users to construct their was used to import those terms to OPMI (if the own sets of omop2obo mappings. import has not been done before). For those Among >100 ontologies in the Open OMOP elements that cannot be mapped to any Biomedical Ontology (OBO) library, the OBO reference ontology, we generated new terms Ontology of Precision Medicine and Investigation and defined them with OPMI namespace based on (OPMI) is an ontology in the domain of precision specific ontology design patterns. The OPMI medicine and investigation [9, 10]. Following the ontology editing was performed using Protege- OBO ontology principles (e.g., openness and OWL editor [16], and the ontology reasoning was collaboration, OPMI reuses many terms of conducted using the Hermit reasoner [17]. All the existing reference ontologies and include many of terms are aligned under the upper-level Basic its own terms in the field of clinical and Formal Ontology (BFO) [18]. Meanwhile, we translational precision medicine, supporting non- have discussed our project design in different redundant and interoperable ontology scenarios, and community feedback and development [11]. OPMI has been developed and 2 comments were obtained to adjust our definitions adverse event model based on the OMOP CDM and design. logic and available data formats. The third use case is the usage of OMOP-2-OPMI to study N3C 2.3. Download and license COVID-19 related clinical data. The OMOP-2-OPMI GitHub web page is: 3. Results https://github.com/OPMI/OMOP-2-OPMI. The 3.1. General OMOP CDM source code of the Omop2Opml.owl file is openly available at this GitHub website for downloading. ontologization architecture The OWL file is generated primarily by extracting the OMOP CDM-related terms and associated Figure 1 represents the hierarchical structure relations from the OPMI using Ontofox [15]. of the OMOP-2-OPMI, which is the Considering the usage of OPMI as the platform ontologization of the OMOP CDM using the for the OMOP CDM mapping, the OMOP-2- OPMI as the ontology platform. Specifically, all OPMI source page is designated as a repository the terms are aligned under the Basic Formal under the general OPMI organization in GitHub. Ontology (BFO) [18], an ISO-approved upper Meanwhile, the OMOP-2-OPMI repository level ontology [19]. BFO includes two branches: has also stored related data files including our continuants and occurrents. Continuants cover cleanup spreadsheets of the mapping details time-independent entities including material available at: https://github.com/OPMI/OMOP-2- entities, quality, realizable entities such as OPMI/tree/main/docs. disposition, and information content entities. Occurrents are time-dependent entities including temporal region and processes. All the OMOP 2.4. Use case studies CDM elements can be categorized under these two categories (Figure 1). BFO has been used by Three use cases are developed and discussed in over 300 ontologies. The alignment with BFO this study. Specifically, the first use case is about allows us to integrate our ontology with the large the OMOP data standardization and inference. number of other ontologies, supporting data The second use case is the development of an interoperability. Figure 1: OMOP-2-OPMI top level hierarchical structure and representative terms. Ontology names are highlighted with different colors. Ontology-mapped OMOP terms are also provided. (i.e., visit/condition/procedure occurrences, and Figure 2 is a simplified high level OMOP-2- drug/device exposure) and the observation OPMI ontology design pattern (ODP) that covers process, which are all under BFO:process (Figure the major elements in 11 OMOP tables. 1). The observation happens during a specific Specifically, the person (usually here it refers to observation period. The person is also the target patient in OMOP) is centric to the ODP. The of measurement. A specimen derives from some person participates in five medical occurrences organ or tissue of the person. The person has 3 different phenotypes, and death is a specific 1 lists ontology mapped CDM element terms from phenotype (Figure 2). 10 representative OMOP tables. Our current mapping primarily covers those clinical data tables and health system data tables. We have not yet included the Metadata Tables, Vocabulary Tables, Standardized Derived Tables except for Episode, and the Cost table which belongs in the Health Economics Data Tables category. These missing tables do not directly involve clinical investigation, which is our current focus. Also as shown in Table 2, many terms are not mapped to ontology. Most of these missing terms are various “source value” or source concept ID terms. Throughout OMOP CDM, Figure 2: General ontology design pattern that there are similar terms representing various links CDM elements from 11 OMOP tables. Note source concepts and source values. In the OMOP one box covers five OMOP occurrence/exposure structure, a source concept set organizes terms tables. Mapped ontology terms are also labeled. into groups called source value sets. A value set (e.g., ‘procedure_source_value’) is a set of codes whose context and usage are defined by one or 3.2. OMOP-2-OPMI statistics more code systems in which the clinical data came from. However, the organization of value sets is A total of 165 terms from 15 OMOP CDM not often ontology-based. In most cases, we have tables has been mapped to OPMI, with 46 terms decided to not incorporate terms for “source newly generated with OPMI namespace and the concept” and “source value” sets until we figure other terms reported from OBO reference out a place for these terms to make sense ontologies. In addition to the 11 tables listed in ontologically within OPMI. In our ontologization, Figure 2, the other four tables are Care Site, Payer we have also included specific source value terms Plan Period, Episode, and Location, which are not as seen in Table 1 and detailed later in the included in Figure 2 to simplify that figure. Table manuscript. Table 1. CDM terms from 10 representative OMOP tables mapped to OPMI Selected OMOP Mapped Mapped Ontology Term Examples tables OMOP terms PERSON 13/19* person ID (OPMI_0000470), gender (PATO_0001894), year of birth (OPMI_0000473), race (NCIT_C17049) PROVIDER 9/13 care provider (OPMI_0000163), National Provider Identifier (OPMI_0000503), DEA identifier (OPMI_0000504) SPECIMEN 6/15 specimen ID (OBI_0001616), date of specimen collection (OBIB_0000714), anatomical structure (UBERON_0000061) VISIT 26/17 visit occurrence (OPMI_0000482), visit start date (OPMI_0000487), preceding OCCURRENCE visit occurrence (OPMI_0000492) PROCEDURE 13/16 procedure (NCIT_C25218), procedure start date (OPMI_0000508), procedure OCCURRENCE end date (OPMI_0000510) DRUG 18/23 drug exposure (OPMI_0000572), drug product (DRON_00000005) drug EXPOSURE exposure start time (OPMI_0000565) CONDITION 38/16 condition occurrence (OPMI_0000527), medical condition status OCCURRENCE (OPMI_0000533), admission diagnosis status (OPMI_0000542) DEVICE 7/15 device exposure (OPMI_0000554), device (OBI_0000968), device exposure 4 EXPOSURE start date (OPMI_0000562) MEASUREMENT 11/20 clinical measurement identifier (OPMI_0000582), measurement time (OPMI_0000579), measurement unit label (IAO_0000003) OBSERVATION 5/6 observation period start date (OPMI_0000577), PERIOD observation period end date (OPMI_0000578), Note: *13/19 represents that 13 out of 19 OMOP CDM terms in the specific category have been mapped to terms in the OPMI ontology. The unmapped terms are primarily those terms related to “source value”. More terms in the visit/condition occurrences are mapped because some specific source value terms are ontologized. In addition to source values or source concept IDs, there are also many terms in OMOP CDM not yet ontologized. The reasons of such imcompleteness include the lack of necessity of many terms, and the complexity of many other terms in terms of ontology modeling. We will continue this work later, ideally by involving more collaboration and discussion with the ontology and clinical informatics communities. Table 2. Ontology mapping of OMOP CDM terms by element types OMOP OMOP percent types terms mapped mapped Figure 3: Modeling of 5 medical occurrence _id 23 19 82.61% categories and 11 specific visit occurrences. _date 34 27 79.41% _concept_id 41 29 70.73% In two of the five OMOP tables, Visit Occurrence and Condition Occurrence, in _concept_name 30 16 53.33% addition to mapping the elements in original _source_concept_id 17 1 5.88% tables (Table 1), we also added some terms from _source_value 34 1 2.94% the supporting OMOP vocabularies for Total 179 93 51.96% developing a complete semantic model. In the case of Visit Occurrence, the extra terms are due to the ontologization of 11 types of visit Next we will focus on a few major ontology occurrences (e.g., ‘emergency room visit’, ‘home modeling topics to show how we model and visit’) that are originally not defined in OMOP’s ontologize the OMOP CDM elements. CDM model and instead are from the supporting OMOP vocabularies identified on the Athena 3.3. Ontologization of OMOP program. We have ontologized such terms under medical occurrences ‘visit occurrence’ (OPMI) (Figure 3). These terms represent the overarching types of encounters By examining the OMOP CDM elements, we between a person and the healthcare system, found that five OMOP tables can be categorized which are adopted in most healthcare systems under an ontology class called ‘medical worldwide. occurrence’, which is defined as a process event In the case of Condition Occurrence, the extra that a patient experiences over a period of time 22 terms come from the incorporation of medical (Figure 3). These five OMOP tables are: condition statuses (e.g., ‘admission diagnosis’, ‘cause of death’, and ‘confirmed diagnosis’), ‘condition occurrence’, ‘device exposure’, ‘drug exposure’, ‘procedure occurrence’, and ‘visit which were defined by OMOP and searchable in occurrence’ (Figure 3). Athena. In OMOP, a medical condition status denotes the stages of a patient’s diagnosis, not the actual state of the disease by itself. OPMI represents these medical condition statuses in two 5 strategies. First, OPMI includes a term called current status of the patient at a specific stage. For ‘medical condition status’ under the ‘status’ term, example, ‘admission diagnosis status’ represents which is a subclass of BFO:‘realizable entity’. In the status at which a person is diagnosed at the this classification, a medical diagnosis status, admission stage. On the other hand, as the data such as admission diagnosis, represents a patient item, the ‘admission diagnosis’ indicates the diagnosis status such as the status of diagnosis at conclusion or outcome of the diagnosis process at the time when the patient is admitted to the the stage of patient admission. A diagnosis hospital. conclusion made at the admission or discharge We have also adopted the OGMS:diagnosis stage may be the same or different. classification and defines various diagnosis types Meanwhile, the diagnosis clinical data type vs under the OGMS:diagnosis (Figure 4). According the diagnosis medical condition status are closely to the Ontology for General Medical Science related. In OPMI, we propose to generate a (OGMS), diagnosis (OGMS_0000073) is a relation term called ‘has status content’, which subclass of clinical data item and represents the represents a relation between a status and an conclusion of a diagnostic process. Based on the information content entity where the status has its OMOP classification, OPMI has defined different content information defined by the information categories of diagnosis, including ‘admission content entity. For example, we can define an diagnosis’, ‘primary diagnosis’, ‘secondary axiom that links a diagnosis status to a diagnosis diagnosis’, and ‘death diagnosis’, etc. (Figure 4). data item: These specific diagnosis types are commonly ‘admission diagnosis status’: ‘has status used at the clinical setting. The classification of content’ some ‘admission diagnosis’ these diagnosis types facilitates the clinical data However, such duplicated representation may annotations. not be needed. It is possible to just define ‘admission diagnosis status’ and remove the term ‘admission diagnosis’. We will examine more use cases and discuss with the ontology and medical informatics communities on this regard. 3.4. Ontologization of temporal date/time in OMOP To ontologically represent various entities denoting time that can be found throughout OMOP, we have mapped 24 temporal terms from 6 tables. The OMOP tables that have temporal terms ontologized are Visit Occurrence, Device Exposure, Drug Exposure, Procedure Occurrence, Condition Occurrence, and Person. For all tables but Person, the entities are ontologized with temporal terms for -start date, -start datetime, -end Figure 4: Modeling of different medical diagnosis date, and -end datetime. Meanwhile, temporal under the OGMS:diagnosis, which is a subclass of terms related to the Person table are instead clinical data item. ontologized with more familiar terms which are ‘birth datetime’, ‘day of birth’, ‘month of birth’, As OPMI separates diagnosis clinical data and ‘year of birth’. All temporal terms are type vs the diagnosis medical condition status, we grouped under a higher level term for a better can define different diagnoses and diagnosis organizational purpose (e.g., ‘visit start statuses. For example, ‘discharge diagnosis date/datetime’, ‘end date/datetime’ are all status’, ‘referral diagnosis status’, and ‘admission grouped under ‘visit temporal region’) (Figure 5). diagnosis status’ are realizable entities, and ‘discharge diagnosis, ‘referral diagnosis status’, and ‘admission diagnosis’ are data items. The main benefit of separate representation of status and data is the semantic separation and clarity. The medical condition status represents the 6 In OPMI, type_concepts are mapped as various terms under ‘provenance of record’, a class under ‘information content entity’. So far, we have generated 12 terms for the provenance of records for 12 corresponding entities of OMOP CDM tables. The provenance of records is dedicated for each corresponding OMOP entity since the sources of the entries can vary across different fields. Meanwhile, OPMI also defines most of the records for the OMOP provenance purposes under ‘electronic health record’, such as ‘electronic medical visit record,’ ‘electronic death record,’ ‘electronic device record,’ etc. (Figure 6). The users can choose the usage of these electronic health records as the sources of the data collected to the OMOP database. Note that not all the Figure 5: OPMI modeling of date and time used provenance records are electronic health records in OMOP CDM. (EHR). For example, in addition to the record from an EHR system, the measurement record might also come from an insurance claim, 3.5. Ontologization of entity registry, or other sources. identifiers in OMOP CDM In OMOP, fields with the suffix “_id_” usually denote identifiers, which function as primary keys in their respective OMOP tables along with other supporting entities (e.g., person_id in Person table). These identifiers can also be used as foreign keys to connect other related OMOP tables (e.g., person_id to connect Provider and Care Site tables). OPMI has ontologized OMOP CDM related identifiers under the class of ‘centrally registered identifier’, a subclass under ‘information content entity’. Example identifiers defined include Figure 6: OPMI modeling of different records ‘person ID’, ‘care site identifier’, ‘clinical used as data provenance in OMOP CDM. measurement identifier’, ‘DEA identifier’ and Next, we will focus on the description of three ‘National Provider Identifier’. These identifiers use cases of the OMOP-2-OPMI approach. identify assets belonging to different but centrally registered local databases. 3.7. Use case 1: Ontology- level data standardization 3.6. Ontologization of provenance records in OMOP The first use case is rooted in the nature of ontology. As an open access ontology following In OMOP, most entities from various tables the OBO ontology development principles, have their own “type_concept” terms, which OMOP-2-OPMI provides the standard indicate the provenance, or the source of the representation and definitions of the OMOP CDM record in which it comes from. For instance, drug mapped terms and the axioms among these terms. exposure entries could come from either The OMOP-2-OPMI ontology terms can be used prescriptions list or self-reported by patients, the to support standardized clinical data provenance of which can differ from a patient’s representation and annotation. The semantic measurement records. relations among the OMOP CDM terms and their associated other terms provide solid semantic 7 associations, which addresses the OMOP CDM conditions of different phenotypes that are the drawback of weak semantics. outcomes of specific adverse events (Figure 7). The ontologized terms are also interoperable. For example, the Coronavirus Infectious Disease Ontology (CIDO), a biomedical ontology in the domain of coronavirus diseases [20], has imported the OMOP-2-OPMI ontology contents. The contents of OMOP-2-OPMI fit seamlessly with the other CIDO contents, providing another demonstration of the ontology-supported knowledge and data interoperability, sharing, and integration. It is also possible to use the some ontology terms for mapping to the other CDMs such as PCORnet [4] and CDISC [5], which will be explored in the future. Such interoperable ontology representation also supports data and knowledge inferencing. Figure 7: General OMOP-AE model based on This is also rooted from the nature of ontology. OMOP-2-OPMI. The red boxes represent OMOP The following two other use cases provide such tables and their mapped ontology terms. The demonstrations. black boxes are added ontology representation to fill up the gaps for adverse event modeling. *, 3.8. Use case 2: Adverse OMOP uses SMOMED-CT concepts for disease or event modeling and analysis symptom representation. These can be mapped to Human Phenotype Ontology (HP) terms. Another use case of the OMOP CDM ontologization is the modeling of adverse events Our original OPMI conference proceeding (AEs) post medical intervention. The OMOP paper presented a use case study of identifying CDM does not include AE per se. However, by and analyzing the acute kidney injury (AKI) AE specific modeling, we can find the OMOP CDM following heart surgery [9]. Using OHDSI data data can be processed to support specific AE provided by the IQVIA Pharmetric Plus database, identification and analysis. our OHDSI cohort study identified a total of Figure 7 is a general OMOP-AE ontology 15,548 patients that fulfilled our predefined model design pattern, which follows the AE definition of AKI AE following heart surgery. Specific by the Ontology of Adverse Events (OAE) [21]. patterns were identified. For example, 72% of the According to the OAE, an adverse event (AE) is a identified patients were male and 28% were pathological bodily process that occurs following female patients. Over 78% of these AE cases some medical intervention [21]. In order to model occurred in patients aged greater than 55 years AEs with OMOP data, we need to identify the old. Many phenotypes, such as coronary medical intervention vs. adverse events to be arteriosclerosis, kidney disease, pain, dyspnea, mapped in OMOP. By examining all the five hyperlipidemia, and Type II diabetes, were found medical occurrence types defined in OMOP, only in these patients as well [9]. three of them are considered as medical Our OMOP AE model is a very general model interventions: Drug Exposure, Device Exposure, in that it can be used to study specific adverse and Procedure Occurrence (e.g., surgical event profiles following various medical procedure). Vaccination can be considered as a interventions including different drug/medicine special drug exposure. exposure and procedure occurrence. We are Note that the visit occurrence and condition currently applying such a strategy to design a occurrence are regarded as natural occurrence pattern for identifying and analyzing the vaccine events without medical intervention. Based on the and drug AEs in COVID-19 patients using the AE definition, contracting a natural infection is N3C data. Note that if a patient contracted not an AE since the patient does not receive an COVID-19 in a natural environment, the patient adverse outcome after a medical intervention. has a condition, which is not an adverse event However, the condition occurrence may include (because an AE is always associated with medical intervention). However, the occurrence of new 8 phenotypes after medical treatment on these The above two studies are currently ongoing COVID-19 patients are considered AEs. and we expect to have more specific results available in near future. 3.9. Use case 3: COVID-19 clinical data standardization, 4. Discussion modeling, and analysis This manuscript has made two main contributions. First, we report our systematic In addition to the import of the OMOP-2- survey and ontologization of the OMOP CDM OPMI to CIDO and the study of COVID-19 elements using the OPMI ontology. The Omop- associated AE modeling and analysis as described 2Opmi.owl file is the OWL file that includes only above, we are also applying the OMOP-2-OPMI the OMOP CDM-related ontology terms, their for more COVID-19 clinical data modeling and directly associated terms (e.g., their parent terms), analysis. Two data resources for our OMOP-2- and the semantic relations between these terms OPMI based studies are the literature reports and that are presented as ontology axioms. Second, we N3C clinical data. presented three categories of use cases of our One specific use case is the study of the OMOP CDM ontologization, including ontology- relation between the COVID-19 infection and the level OMOP CDM element standardization and increased risk for kidney diseases. For example, inferencing, adverse event modeling and analysis, acute kidney injury (AKI) is a significant and COVID-19 clinical data studies. Overall, our complication of COVID-19. The incidence of systematic ontologization of the OMOP CDM AKI in hospitalized patients varies from 0.5% to complements and empowers the OMOP CDM 75%. The mortality rate for patients with kidney system, providing a new way of supporting disease is also significantly higher than the systematic clinical data interoperability, sharing, general infected population. However, the big and integration. variation of AKI incidence in COVID-19 patients A similar and related system is OMOP2OBO, appears to depend on many factors such as race, a systematic mapping tool that maps OMOP region, and disease severity. The N3C cohort data related terms to OBO ontologies [7]. The terms is being used to detect, compare, and analyze the mapped in OMOP2OBO cover 8 OBO ontologies, occurrences of kidney disease following COVID- including Cell Ontology (CL), ChEBI chemical 19 infection. The OMOP-2-OPMI model, entity ontology, Human Phenotype Ontology together with the OMOP2OBO, can be used to (HP), MONDO disease Ontology, NCBI support data modeling, integration, and analysis. Taxonomy Ontology (NCBITaxon), Protein The integrated data can also be further used for Ontology (PR), Uberon anatomy ontology, and machine learning tool development for kidney Vaccine Ontology (VO). While OMOP2OBO disease prediction following COVID-19 includes the mapping of over 100,000 terms in the prediction. We have registered for an N3C OMOP terminology system, it does not cover the program to perform related research. OMOP CDM elements in the over 10 basic Another use case in this category is the OMOP tables. Instead, OMOP-2-OPMI focuses application of OMOP-2-OPMI and CIDO for on the core OMOP CDM level mapping and secondary literature data analysis and knowledge representation. In addition to ontology term representation. There have been a big number of mapping, since many high level terms in OMOP COVID-19 studies reported in the literature, many CDM are not yet represented in OBO ontologies, of which involve the usage of OMOP CDM we have taken extensive effort to generate many model. For example, one study examined the new terms in OPMI. We have also generated association between immune dysfunction and ontological relations among these OMOP CDM COVID-19 breakthrough infection after SARS- elements using the OPMI ontology platform. CoV-2 vaccination in the US using N3C data [22]. Overall, OMOP2OBO and OMOP-2-OPMI are The N3C data and the results out of the data complementary in that they map and integrate analysis can both be modeled, annotated, and OMOP data from different aspects. represented using ontology including our OMOP- There are still many issues to consider in our 2-OPMI and CIDO. ontologization. For example, we presented two types of methods for representing medical condition statuses and two types of methods of 9 representing provenance records in our work. Center Joint Institute for Clinical and Since most medical condition statuses are Translational Research (U072807). We appreciate different types of diagnosis, such status the discussion and comments from the ontology representations can be defined under “status”, and OMOP societies including Dr. Asiyah Yu Lin which is defined as a BFO:‘realizable entity’, or and Dr. Andrew Williams. under OGMS:diagnosis, which is basically a type of clinical data item. Similarly, for the provenance 6. References records, they can be represented under provenance itself or under electronic health record. The ICBO-2022 conference will provide [1] E. A. Voss, R. Makadia, A. Matcho, Q. us a discussion platform to discuss the pros and Ma, C. Knoll, M. Schuemie, et al., cons of different representation styles. 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Furthermore, we can representation of OMOP CDM using the OBO framework," presented at the 2018 develop new models to apply OMOP CDM to study other topics such as long COVID and the OHDSI Symposium, Bethesda North effects of different variables to the disease Marriott, Bethesda, MD, 2018. outcomes. [4] F. S. Collins, K. L. Hudson, J. P. Briggs, One future project is to map the CDM terms and M. S. Lauer, "PCORnet: turning a from other systems, including PCORnet [4] and dream into reality," J Am Med Inform Assoc, vol. 21, pp. 576-7, Jul-Aug 2014. CDISC [5], to the OPMI ontology using the same [5] S. Hume, J. Aerts, S. Sarnikar, and V. OMOP-2-OPMI development strategy. These different CDMs are overlapped. For example, Huser, "Current applications and future There are similarities between the organizations directions for the CDISC Operational of OMOP and PCORnet CDMs, evidenced by the Data Model standard: A methodological overlaps of certain tables such as Demographic, review," J Biomed Inform, vol. 60, pp. 352-62, Apr 2016. Procedures, or Condition [23]. When all these CDM elements and relations are mapped to the [6] COVID-19 Clinical Data Warehouse same OPMI structure, we can integrate all the data Data Dictionary Based on OMOP using different CDMs, leading to compatible and Common Data Model Specifications Version 5.3 Available: interoperable clinical and observational data standardization and integration. A recent study https://ncats.nih.gov/files/OMOP_CDM_ reports the development of an ETL tool for COVID.pdf converting the PCORnet CDM into OMOP CDM [7] T. J. Callahan, J. M. Wyrwa, N. A. to facilitate the COVID-19 data integration [24]. Vasilevsky, and P. N. 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