Wu, H;
Wang, M;
Zeng, Q;
Chen, W;
Nind, T;
Jefferson, E;
Bennie, M;
... Robertson, D; + view all
(2020)
Knowledge Driven Phenotyping.
Studies in Health Technology and Informatics
, 270
pp. 1327-1328.
10.3233/SHTI200425.
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Abstract
Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.
| Type: | Article |
|---|---|
| Title: | Knowledge Driven Phenotyping |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/SHTI200425 |
| Publisher version: | https://doi.org/10.3233/SHTI200425 |
| Language: | English |
| Additional information: | © 2020 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). |
| Keywords: | data integration, health data, ontology, phenotype computation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10105352 |
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