Ontologies in big health data analytics: application to routine clinical data

Journal article

Ontologies are an important big-data analytics tool. Historically code lists were created by domain experts and mapped between different coding systems. Ontologies allow us to develop better representations of clinical concepts, data and facilitate better data extracts from routine clinical data. It also makes the process of case identification and key outcome measures transparent. We describe a process we have operationalised in our research. We use ontologies to resolve the semantics of complex health care data. The use of the method is demonstrated through a pregnancy case identification method. Pregnancy data are recorded in different coding systems and stored in different general practice systems; and pregnancy has its own complexities in that not all pregnancies proceed to term, they have different lengths and involve multiple providers of health care.

Authors: Liyanage, H; Williams, J; Byford, R; Stergioulas, L; De Lusignan, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOS Press
• Journal: Decision Support Systems and Education
• Volume: 255
• Pages: 65-69
• Series: Studies in Health Technology and Informatics
• Publication date: 2018-01-01
• DOI: 10.3233/978-1-61499-921-8-65
• EISSN: 1879-8365
• ISSN: 0926-9630
• Pmid: 30306908
• ISBN: 9781614999201
• Language: English
• Keywords: semantics; big data; vocabulary, controlled; information storage and retrieval; health; data science