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Conference Publication

CliniPhenome: Clinical and Phenotypic Annotation Database: oral presentation

Host Publication: 2nd International Conference on Health Informatics and Technology

Authors: D. Sengupta, D. Croes, S. Kapoor, S. Van Dooren, M. Bonduelle and A. Nowé

Publisher: Journal of Health & Medical Informatics

Publication Date: Jul. 2015

Number of Pages: 1


Abstract:

As a part of the BRiDGEIris project, a clinical database CliniPhenome, has been developed that supports clinical and phenotypic annotation for patients. The project aims to develop a big data platform that can be used for sharing genomic and clinical data and support discovery studies in clinical genomics. A part of this goal was attained by the proposal of a generalized clinical schemadesign along with a merged ontology schema that provides cross-referencing possibilities. Also, introduction of the controlled vocabulary and data structure answers for the prevalent problem of plain text storage in the existing clinical databases, which causes impediment during data analysis. CliniPhenome not only supports the collection of patient data but also phenotypic and clinicalannotation. Phenotypic annotation is supported by autogenously designed ontology that supports for Human Phenotype Ontology (HPO), Online Mendelian Inheritance in Man (OMIM) and Orphanet. Clinical annotation is currently supported by LOINC and SNOMED, with eventual assimilation of ICDᆞ into it. It is a PHP based application with an easily operated interface that runs on aweb browser and is facilitated by a MySQL database. A generalized schema design has been proposed, that ensures improved empathy among clinicians and researchers. Besides, it also features data migration utility that supports exchange of de-identified data in XML or JSON structure among the collaborators. In conclusion, CliniPhenome features streamlining of clinical workflow, efficient data entry and retrieval, improved diagnosis, cross-referencing of patient phenotype with clinical annotation, and sharing of de-identified patient data for research purposes.

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