Dataset Information

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

ABSTRACT: This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges. Widely-adopted ontologies such as SNOMED-CT are used to represent the semantics of the data and to express concepts in the application filters, facilitating data sharing and collaboration on joint research questions in large communities of clinical users. The application supports patient data exploration and efficient collaboration in multi-site, heterogeneous and distributed data environments.

SUBMITTER: Bucur A

PROVIDER: S-EPMC5001740 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

Bucur Anca A van Leeuwen Jasper J Chen Njin-Zu NZ Claerhout Brecht B de Schepper Kristof K Perez-Rey David D Paraiso-Medina Sergio S Alonso-Calvo Raul R Mehta Keyur K Krykwinski Cyril C

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20160720

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition ...[more]

PMID: 27570644

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Project description:BackgroundDespite increasing availability of environmental health science (EHS) data, development, and implementation of relevant semantic standards, such as ontologies or hierarchical vocabularies, has lagged. Consequently, integration and analysis of information needed to better model environmental influences on human health remains a significant challenge.ObjectivesWe aimed to identify a committed community and mechanisms needed to develop EHS semantic standards that will advance understanding about the impacts of environmental exposures on human disease.MethodsThe National Institute of Environmental Health Sciences sponsored the "Workshop for the Development of a Framework for Environmental Health Science Language" hosted at North Carolina State University on 15-16 September 2014. Through the assembly of data generators, users, publishers, and funders, we aimed to develop a foundation for enabling the development of community-based and data-driven standards that will ultimately improve standardization, sharing, and interoperability of EHS information.DiscussionCreating and maintaining an EHS common language is a continuous and iterative process, requiring community building around research interests and needs, enabling integration and reuse of existing data, and providing a low barrier of access for researchers needing to use or extend such a resource.ConclusionsRecommendations included developing a community-supported web-based toolkit that would enable a) collaborative development of EHS research questions and use cases, b) construction of user-friendly tools for searching and extending existing semantic resources, c) education and guidance about standards and their implementation, and d) creation of a plan for governance and sustainability.CitationMattingly CJ, Boyles R, Lawler CP, Haugen AC, Dearry A, Haendel M. 2016. Laying a community-based foundation for data-driven semantic standards in environmental health sciences. Environ Health Perspect 124:1136-1140;?http://dx.doi.org/10.1289/ehp.1510438.

Project description:BackgroundThe harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis.ObjectiveThis study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses.MethodsWe propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data.ResultsWe imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including "Patient," "Encounter," "Condition" (diagnoses specified using International Classification of Diseases codes), "Procedure," and "Observation" (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs).ConclusionsThis study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients' privacy while being integrated with existing open source data analysis tools currently being developed across Germany.

Project description:BackgroundFetal growth restriction is a major complication of pregnancy and is associated with stillbirth, infant death and child morbidity. Ultrasound monitoring of pregnancy is becoming more common in Africa for fetal growth monitoring in clinical care and research, but many countries have no national growth charts. We evaluated the new international fetal growth standards from INTERGROWTH-21st and WHO in a cohort from southern Benin.MethodsRepeated ultrasound and clinical data were collected in women from the preconceptional RECIPAL cohort (241 women with singleton pregnancies, 964 ultrasounds). We modelled fetal biometric parameters including abdominal circumference (AC) and estimated fetal weight (EFW) and compared centiles to INTERGROWTH-21st and WHO standards, using the Bland and Altman method to assess agreement. For EFW, we used INTERGROWTH-21st standards based on their EFW formula (IG21st) as well as a recent update using Hadlock's EFW formula (IG21hl). Proportions of fetuses with measurements under the 10th percentile were compared.ResultsMaternal malaria and anaemia prevalence was 43% and 69% respectively and 11% of women were primigravid. Overall, the centiles in the RECIPAL cohort were higher than that of INTERGROWTH-21st and closer to that of WHO. Consequently, the proportion of fetuses under 10th percentile thresholds was systematically lower when applying IG21st compared to WHO standards. At 27-31 weeks and 33-38 weeks, respectively, 7.4% and 5.6% of fetuses had EFW <10th percentile using IG21hl standards versus 10.7% and 11.6% using WHO standards.ConclusionDespite high anemia and malaria prevalence in the cohort, IG21st and WHO standards did not identify higher than expected proportions of fetuses under the 10th percentiles of ultrasound parameters or EFW. The proportions of fetuses under the 10th percentile threshold for IG21st charts were particularly low, raising questions about its use to identify growth-restricted fetuses in Africa.

Dataset Information

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

Publications

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

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