@inproceedings{d4069befcd994e51b4150882f131a069,
title = "Knowledge driven phenotyping: Proceedings of MIE 2020",
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.",
keywords = "data integration, health data, ontology, phenotype computation",
author = "Honghan Wu and Minhong Wang and Qianyi Zeng and Wenjun Chen and Thomas Nind and Emily Jefferson and Marion Bennie and Corri Black and Pan, {Jeff Z.} and Cathie Sudlow and Dave Robertson",
year = "2020",
month = jun,
day = "16",
doi = "10.3233/SHTI200425",
language = "English",
isbn = "9781643680828",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1327--1328",
editor = "Pape-Haugaard, {Louise B.} and Christian Lovis and Madsen, {Inge Cort} and Patrick Weber and Nielsen, {Per Hostrup} and Philip Scott",
booktitle = "Digital Personalized Health and Medicine",
address = "Netherlands",
note = "30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",
}