Knowledge driven phenotyping : Proceedings of MIE 2020

Wu, Honghan and Wang, Minhong and Zeng, Qianyi and Chen, Wenjun and Nind, Thomas and Jefferson, Emily and Bennie, Marion and Black, Corri and Pan, Jeff Z. and Sudlow, Cathie and Robertson, Dave; Pape-Haugaard, Louise B. and Lovis, Christian and Madsen, Inge Cort and Weber, Patrick and Nielsen, Per Hostrup and Scott, Philip, eds. (2020) Knowledge driven phenotyping : Proceedings of MIE 2020. In: Digital Personalized Health and Medicine. Studies in Health Technology and Informatics . IOS Press, CHE, pp. 1327-1328. ISBN 9781643680828

[img]
Preview
Text (Wu-etal-MIE2020-Knowledge-driven-phenotyping)
Wu_etal_MIE2020_Knowledge_driven_phenotyping.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial 4.0 logo

Download (114kB)| Preview

    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.