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. 2016 Jul 20:2016:25-32.
eCollection 2016.

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

Anca Bucur  1 , Jasper van Leeuwen  1 , Njin-Zu Chen  1 , Brecht Claerhout  2 , Kristof de Schepper  2 , David Perez-Rey  3 , Sergio Paraiso-Medina  3 , Raul Alonso-Calvo  3 , Keyur Mehta  4 , Cyril Krykwinski  5
Affiliations

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

Anca Bucur et al. AMIA Jt Summits Transl Sci Proc. .

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.

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Figures

Figure 1.
Figure 1.
The main components of the cohort selection application and the relevant services used by the application
Figure 2.
Figure 2.
Visual filters for intuitive selection of ranges of values and of discrete categories. Age range (top) and gender (bottom) of patients in the desired cohort.
Figure 3.
Figure 3.
GUI capabilities for exploring the available datasets and for selecting ontology concepts in the filter templates with the use of autocomplete.
Figure 4.
Figure 4.
GUI capabilities for saving, sharing with other users and exporting cohorts, and for exploring the datasets.
Figure 5.
Figure 5.
Semantic Interoperability Layer (SIL) components for the cohort selection application
Figure 6.
Figure 6.
Anthracyclines versus Epirubicin – reasoning based on SNOMED-CT.
Figure 7.
Figure 7.
Breast cancer patients with no relapse 5 years after diagnosis – temporal conditions. SNOMED-CT codes of concepts represented in SNOMED-CT normal form are used in the DSL fragment.
Figure 8.
Figure 8.
Distribution of ER+ BC patients with metastatic disease based on the target site.
See this image and copyright information in PMC

References

    1. Bucur A, van Leeuwen J, Chen NZ, Claerhout B, de Schepper K, Perez-Rey D, et al. Supporting Patient Screening to Identify Suitable Clinical Trials. In Stud Health Technol Inform. 2014;205:823–7. 2014. - PubMed
    1. Claerhout B, de Schepper K, Pérez-Rey D, Alonso-Calvo R, van Leeuwen J, Bucur A. Leveraging Dynamic Programming Languages for Efficient Implementation of a Patient Cohort Selection Engine. 2013.
    1. van Leeuwen J, Bucur A, Keijser J, Claerhout B, de Schepper K, Pérez-Rey D, et al. Recruitment and feasibility tool: J. Clinical Bioinformatics. 2015
    1. Claerhout B, de Schepper K, Pérez-Rey D, Alonso-Calvo R, van Leeuwen J, Bucur A. Implementing patient recruitment on EURECA semantic integration platform through a Groovy query engine: BIBE. 2013.
    1. Bache R, Taweel A, Miles S, Delaney B. An Eligibility Criteria Query Language for Heterogeneous Data Warehouses. Methods of Information in Medicine. 2014. p. to appear. - PubMed

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