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Transforming and evaluating the UK Biobank to the OMOP common data model for COVID-19 research and beyond

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第1期30卷 103-111页

作者： Papez, Vaclav Moinat, Maxim Voss, Erica A. Bazakou, Sofia Van Winzum, Anne Peviani, Alessia Payralbe, Stefan Kallfelz, Michael Asselbergs, Folkert W. Prieto-Alhambra, Daniel Dobson, Richard J. B. Denaxas, Spiros UCL Inst Hlth Informat London NW1 2DA England Hlth Data Res UK London England The Hyve Utrecht Netherlands Erasmus MC Rotterdam Netherlands Janssen Res & Dev LLC Dept Epidemiol Raritan NJ USA Odysseus Data Serv GmbH Berlin Germany Univ Amsterdam Amsterdam Univ Med Ctr Dept Cardiol Amsterdam Netherlands Univ Oxford Ctr Stat Med NDORMS Oxford England Kings Coll London Inst Psychiat Psychol & Neurosci IoPPN Dept Biostat & Hlth Informat London England British Heart Fdn Data Sci Ctr London England UCL Hosp NIHR Biomed Res Ctr BRC London England

Objective The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. Materials and Methods We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. Results We identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medications 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6 465 399 (89.2%) procedures, 337 896 333 primary care diagnoses (CTV3, SNOMED-CT), 139 966 587 (98.74%) prescriptions (dm+d) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. Discussion and Conclusion Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK

关键词： electronic health records medical ontologies phenotyping OMOP common data model

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Building an active medical product safety surveillance system in Taiwan: Adaptation of the US Sentinel System common data model structure to the National Health Insurance Research database in Taiwan

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2021年第1期30卷 97-101页

作者： Huang, Kelly Lin, Fang-Ju Ou, Huang-Tz Hsu, Chien-Ning Huang, Ling-Ya Wang, Chi-Chuan Toh, Sengwee Natl Taiwan Univ Coll Med Sch Pharm Room 20233Linsen S Rd Taipei 100 Taiwan Natl Taiwan Univ Coll Med Grad Inst Clin Pharm Taipei Taiwan Natl Taiwan Univ Hosp Dept Pharm Taipei Taiwan Natl Cheng Kung Univ Inst Clin Pharm & Pharmaceut Sci Tainan Taiwan Kaohsiung Chang Gung Mem Hosp Dept Pharm Kaohsiung Taiwan Kaohsiung Med Univ Coll Pharm Kaohsiung Taiwan Harvard Med Sch Dept Populat Med Boston MA 02115 USA Harvard Pilgrim Hlth Care Inst Boston MA USA

Purpose Using real-world data to support regulatory decision has become a global movement. However, a robust platform for active surveillance of medical product safety has not been established in Taiwan. Methods Following the common data model structure of the U.S. Food and Drug Administration's Sentinel System, we built the Taiwan Sentinel data model (TSDM) using the National Health Insurance Research database with longitudinal claims data from 23 million individuals, linked death and cause of death data from a national registry, and linked electronic health record data from a delivery system. We examined the conversion of the TSDM using the Sentinel data Quality Review and Characterization Programs in a sample of sex- and age-stratified cohort of 3 million individuals. Results The TSDM fulfilled the requirements of data quality assurance. Only about 6% of sex and 0.0007% of birth year were missing, and <0.001% of date data had illogical values. Conclusions The TSDM-converted database could be a valuable data resource for domestic pharmacovigilance analysis in Taiwan and cross-country evaluation.

关键词： common data model pharmacoepidemiology pharmacovigilance sentinel initiative Taiwan

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Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP common data model and Its Use for COVID-19 Research

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CLINICAL EPIDEMIOLOGY 2023年 15卷 969-986页

作者： Raventos, Berta Fernandez-Bertolin, Sergio Aragon, Maria Voss, Erica A. Blacketer, Clair Mendez-Boo, Leonardo Recalde, Martina Roel, Elena Pistillo, Andrea Reyes, Carlen van Sandijk, Sebastiaan Halvorsen, Lars Rijnbeek, Peter R. Burn, Edward Duarte-Salles, Talita CSIC ICN2 Barcelona Spain Univ Autonoma Barcelona Campus Bellaterra Cerdanyola Del Valles Barcelona Spain Janssen Pharmaceut Res & Dev Titusville NJ USA Erasmus Univ Med Ctr Dept Med Informat Rotterdam Netherlands OHDSI Collaborators Observat Hlth Data Sci & Informat OHDSI New York NY USA Inst Catala Salut Equipo Atenc Primaria Sanllehy Barcelona Spain Univ Pompeu Fabra Barcelona Spain NET4GAS S R O Prague Czech Republic Medimprove Kontich Belgium Univ Oxford Ctr Stat Med Oxford OX3 7LF England Inst Univ Invest AtencioPrimaria Jordi Gol IDIAP Gran Via Corts Catalanes 587 At Barcelona 08007 Spain

Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June ***: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who ***: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

关键词： electronic health records medical ontologies secondary data use common data model OMOP

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Unidata's common data model mapping to the ISO 19123 data model

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EARTH SCIENCE INFORMATICS 2008年第2期1卷 59-78页

作者： Nativi, Stefano Caron, John Domenico, Ben Bigagli, Lorenzo Italian Natl Res Council IMAA Tito Italy Univ Florence Florence Italy UCAR Off Programs Unidata Program Ctr Boulder CO 80307 USA

Access to real-time distributed Earth and Space Science (ESS) information is essential for enabling critical Decision Support Systems (DSS). Thus, data model interoperability between the ESS and DSS communities is a decisive achievement for enabling cyber-infrastructure which aims to serve important societal benefit areas. The ESS community is characterized by a certain heterogeneity, as far as data models are concerned. Recent spatial data infrastructures implement international standards for the data model in order to achieve interoperability and extensibility. This paper presents well-accepted ESS data models, introducing a unified data model called the common data model (CDM). CDM mapping into the corresponding elements of the international standard coverage data model of ISO 19123 is presented and discussed at the abstract level. The mapping of CDM scientific data types to the ISO coverage model is a first step toward interoperability of data systems. This mapping will provide the abstract framework that can be used to unify subsequent efforts to define appropriate conventions along with explicit agreed-upon encoding forms for each data type. As a valuable case in point, the content mapping rules for CDM grid data are discussed addressing a significant example.

关键词： common data model Coverage data model Fluid earth sciences ISO 19123 model mapping Scientific data types

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Feasibility of Applying the OMOP common data model to Traditional Eastern Asian Medicine dataset

Feasibility of Applying the OMOP Common Data Model to Tradit...

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19th World Congress on Medical and Health Informatics (MEDINFO)

作者： Cha, Jiyun Ahn, Eun Kyoung Yoon, Young-Heum Park, Man Young Korea Inst Oriental Med Daejeon 34054 South Korea Natl Inst Korean Med Dev Seoul 04516 South Korea

ISBN: (纸本)9781643684574;9781643684567

To evaluate the feasibility of applying the Observational Medical Outcome Partnership (OMOP) common data model (CDM) to databases of traditional East Asian medicine (TEAM), we composed a TEAM dataset and transformed it to the OMOP CDM. We found that some important TEAM information entities could not be transformed to the OMOP CDM (version 6.0) data fields. We suggest to develop data fields and guideline for transforming TEAM data to the OMOP CDM.

关键词： Observational medical outcome partnership (OMOP) common data model traditional Eastern Asia medicine Korean medicine

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Development of Integrated data Quality Management System for Observational Medical Outcomes Partnership common data model

Development of Integrated Data Quality Management System for...

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19th World Congress on Medical and Health Informatics (MEDINFO)

作者： Oh, Seol Whan Ko, Soo Jeong Im, Yun Seon Jung, Surin Choi, Bo Yeon Kim, Jae Yoon Park, Sunghyeon Choi, Wona Choi, In Young Catholic Univ Korea Dept Med Informat Coll Med Seoul South Korea Catholic Univ Korea Dept Biomed & Hlth Sci Seoul South Korea

ISBN: (纸本)9781643684574;9781643684567

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

关键词： data quality common data model data quality management system

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ARDaC common data model Facilitates data Dissemination and Enables data commons for Modern Clinical Studies

ARDaC Common Data Model Facilitates Data Dissemination and E...

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19th World Congress on Medical and Health Informatics (MEDINFO)

作者： Jin, Nanxin Li, Zuotian Kettler, Carla Yang, Baijian Tu, Wanzhu Su, Jing Indiana Univ Sch Med Biostat & Hlth Data Sci Indianapolis IN 46202 USA Purdue Univ Comp & Informat Technol W Lafayette IN USA Purdue Univ Comp Graph Technol W Lafayette IN USA

ISBN: (纸本)9781643684574;9781643684567

Modern clinical studies collect longitudinal and multimodal data about participants, treatments and responses, biospecimens, and molecular and multiomics data. Such rich and complex data requires new common data models (CDM) to support data dissemination and research collaboration. We have developed the ARDaC CDM for the Alcoholic Hepatitis Network (AlcHepNet) Research data commons (ARDaC) to support clinical studies and translational research in the national AlcHepNet consortium. The ARDaC CDM bridges the gap between the data models used by the AlcHepNet electronic data capture platform (REDCap) and the Genomic data commons (GDC) data model used by the Gen3 data commons framework. It extends the GDC data model for clinical studies;facilitates the harmonization of research data across consortia and programs;and supports the development of the ARDaC. ARDaC CDM is designed as a general and extensible CDM for addressing the needs of modern clinical studies. The ARDaC CDM is available at https://***/DD.

关键词： common data model data commons clinical study

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An Architecture and common data model for Open data-Based Cargo-Tracking in Synchromodal Logistics

An Architecture and Common Data Model for Open Data-Based Ca...

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OnTheMove (OTM) International Federated Conference

作者： Raap, Wouter Bol Iacob, Maria-Eugenia van Sinderen, Marten Piest, Sebastian Univ Twente POB 217 NL-7500 AE Enschede Netherlands CAPE Grp Transportctr 14 NL-7547 RW Enschede Netherlands

ISBN: (纸本)9783319484723;9783319484716

In logistics, questions as "Where is my container?" and "When does my container arrive?" can often not be answered with sufficient precision, which restricts the ability of logistics service providers to be efficient. Since logistics is complex and often involves multiple transportation modes and carriers, improving efficiency and saving costs in the supply chain requires communication between the different parties and the usage of real-time data is critical. Currently, logistics service providers (LSPs) use real-time data to a very limited extent, mainly for tracking the progress of a specific part of a given shipment. This data is retrieved manually from a number of websites and sharing with other actors is not even considered. This leads to lack of end-to end visibility and delays in planning. This research proposes an architecture and a common data model for an integration platform that allows the automated collection of real time container tracking data enabling LSPs to plan more efficient. Currently, there is no common data model available that contains all the information required and enables LSPs to track their shipments real-time. The common data model is designed via a bottom-up approach using results of interviews, observations at different logistics service providers, analyses of open data on websites, and serves the information needs of the business processes involving such data. The model is also validated against industry standards. Based on the proposed architecture a prototype was built that is tested in real operating conditions with a fourth party logistics company.

关键词： Synchromodal logistics common data model Logistics Integration platform Web scraping Open data

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data Quality Assessment for Observational Medical Outcomes Partnership common data model of Multi-Center 33

Data Quality Assessment for Observational Medical Outcomes P...

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33rd Medical Informatics Europe Conference (MIE) - Caring is Sharing - Exploiting the Value in data for Health and Innovation

作者： Oh, Seol Whan Ko, Soo Jeong Im, Yun Seon Jung, Surin Choi, Bo Yeon Kim, Jae Yoon Choi, Wona Choi, In Young Catholic Univ Korea Dept Med Informat Coll Med Seoul South Korea Catholic Univ Korea Dept Biomed & Hlth Sci Seoul South Korea

ISBN: (纸本)9781643683898

关键词： data Quality common data model data Quality Management System

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Implementation of a Geographic Information System with Big data Environment on common data model 8

Implementation of a Geographic Information System with Big D...

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8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Sik, David Csorba, Kristof Ekler, Peter Budapest Univ Technol & Econ Dept Automat & Appl Informat Budapest Hungary

ISBN: (纸本)9781538612644

We introduce an implementation of a GIS (Geographic Information System) research and development subsystem in the CV4SensorHUB (Computer Vision for SensorHUB) framework. The main concept is the usage and connection of open-access online databases such as OpenStreetMaps, Citybik. es, BKK Futar, MAV Elvira and others. These databases cover different fields in the geographic domain. By connecting these databases with MapReduce paradigm and implementing smart algorithms, we are about to create an online tool where users could parametrize their trips around the town, using the live information and then visualize them on a map. For example, with the OpenStreetMaps it is possible to extract the maps, streets, POIs (point of interest), then with the Citybik. es we can get the live data about public bicycle sharing system, and with the BKK Futar we can access the live public transportation data, vehicles, timetables. In this paper, we focus on the data sources, the architecture with its common data model, and the main implementation steps of the system.

关键词： GIS Geospatial MapReduce Big data common data model

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