Knowledge driven phenotyping: Proceedings of MIE 2020

Honghan Wu; Minhong Wang; Qianyi Zeng; Wenjun Chen; Thomas Nind; Emily Jefferson; Marion Bennie; Corri Black; Jeff Z. Pan; Cathie Sudlow; Dave Robertson

doi:10.3233/SHTI200425

Knowledge driven phenotyping: Proceedings of MIE 2020

Honghan Wu, Minhong Wang, Qianyi Zeng, Wenjun Chen, Thomas Nind, Emily Jefferson, Marion Bennie, Corri Black, Jeff Z. Pan, Cathie Sudlow, Dave Robertson

Strathclyde Institute Of Pharmacy And Biomedical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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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.

Original language	English
Title of host publication	Digital Personalized Health and Medicine
Subtitle of host publication	Proceedings of MIE 2020
Editors	Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	1327-1328
Number of pages	2
ISBN (Print)	9781643680828
DOIs	https://doi.org/10.3233/SHTI200425
Publication status	Published - 16 Jun 2020
Event	30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland Duration: 28 Apr 2020 → 1 May 2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	270
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	30th Medical Informatics Europe Conference, MIE 2020
Country	Switzerland
City	Geneva
Period	28/04/20 → 1/05/20

Keywords

data integration
health data
ontology
phenotype computation

Access to Document

10.3233/SHTI200425Licence: CC BY-NC 4.0

Wu-etal-MIE2020-Knowledge-driven-phenotypingFinal published version, 111 KBLicence: CC BY-NC 4.0

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Wu, H., Wang, M., Zeng, Q., Chen, W., Nind, T., Jefferson, E., Bennie, M., Black, C., Pan, J. Z., Sudlow, C., & Robertson, D. (2020). Knowledge driven phenotyping: Proceedings of MIE 2020. In L. B. Pape-Haugaard, C. Lovis, I. C. Madsen, P. Weber, P. H. Nielsen, & P. Scott (Eds.), Digital Personalized Health and Medicine: Proceedings of MIE 2020 (pp. 1327-1328). (Studies in Health Technology and Informatics; Vol. 270). IOS Press. https://doi.org/10.3233/SHTI200425

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

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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",

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Wu, H, Wang, M, Zeng, Q, Chen, W, Nind, T, Jefferson, E, Bennie, M, Black, C, Pan, JZ, Sudlow, C & Robertson, D 2020, Knowledge driven phenotyping: Proceedings of MIE 2020. in LB Pape-Haugaard, C Lovis, IC Madsen, P Weber, PH Nielsen & P Scott (eds), Digital Personalized Health and Medicine: Proceedings of MIE 2020. Studies in Health Technology and Informatics, vol. 270, IOS Press, Amsterdam, pp. 1327-1328, 30th Medical Informatics Europe Conference, MIE 2020, Geneva, Switzerland, 28/04/20. https://doi.org/10.3233/SHTI200425

Knowledge driven phenotyping : Proceedings of MIE 2020. / Wu, Honghan; Wang, Minhong; Zeng, Qianyi; Chen, Wenjun; Nind, Thomas; Jefferson, Emily; Bennie, Marion; Black, Corri; Pan, Jeff Z.; Sudlow, Cathie; Robertson, Dave.

Digital Personalized Health and Medicine: Proceedings of MIE 2020. ed. / Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. Amsterdam : IOS Press, 2020. p. 1327-1328 (Studies in Health Technology and Informatics; Vol. 270).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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T1 - Knowledge driven phenotyping

T2 - 30th Medical Informatics Europe Conference, MIE 2020

AU - Wu, Honghan

AU - Wang, Minhong

AU - Zeng, Qianyi

AU - Chen, Wenjun

AU - Nind, Thomas

AU - Jefferson, Emily

AU - Bennie, Marion

AU - Black, Corri

AU - Pan, Jeff Z.

AU - Sudlow, Cathie

AU - Robertson, Dave

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N2 - 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.

AB - 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.

KW - data integration

KW - health data

KW - ontology

KW - phenotype computation

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A2 - Pape-Haugaard, Louise B.

A2 - Lovis, Christian

A2 - Madsen, Inge Cort

A2 - Weber, Patrick

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A2 - Scott, Philip

PB - IOS Press

CY - Amsterdam

Y2 - 28 April 2020 through 1 May 2020

ER -

Wu H, Wang M, Zeng Q, Chen W, Nind T, Jefferson E et al. Knowledge driven phenotyping: Proceedings of MIE 2020. In Pape-Haugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH, Scott P, editors, Digital Personalized Health and Medicine: Proceedings of MIE 2020. Amsterdam: IOS Press. 2020. p. 1327-1328. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI200425