A knowledge-base for a personalized infectious disease risk prediction system

Retno Vinarti; Lucy Hederman

doi:10.3233/978-1-61499-852-5-531

A knowledge-base for a personalized infectious disease risk prediction system

Retno Vinarti^*
, Lucy Hederman

^*Corresponding author for this work

Trinity College Dublin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

Original language	English
Title of host publication	Building Continents of Knowledge in Oceans of Data
Subtitle of host publication	The Future of Co-Created eHealth - Proceedings of MIE 2018
Editors	Gunnar O. Klein, Daniel Karlsson, Anne Moen, Adrien Ugon
Publisher	IOS Press
Pages	531-535
Number of pages	5
ISBN (Electronic)	9781614998518
DOIs	https://doi.org/10.3233/978-1-61499-852-5-531
Publication status	Published - 2018
Externally published	Yes
Event	40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden Duration: 24 Apr 2018 → 26 Apr 2018

Publication series

Name	Studies in Health Technology and Informatics
Volume	247
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	40th Medical Informatics in Europe Conference, MIE 2018
Country/Territory	Sweden
City	Gothenburg
Period	24/04/18 → 26/04/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Infectious disease
Knowledge-base
Ontology
Risk
Rules

Access to Document

10.3233/978-1-61499-852-5-531

Cite this

Vinarti, R., & Hederman, L. (2018). A knowledge-base for a personalized infectious disease risk prediction system. In G. O. Klein, D. Karlsson, A. Moen, & A. Ugon (Eds.), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018 (pp. 531-535). (Studies in Health Technology and Informatics; Vol. 247). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-531

Vinarti, Retno ; Hederman, Lucy. / A knowledge-base for a personalized infectious disease risk prediction system. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. editor / Gunnar O. Klein ; Daniel Karlsson ; Anne Moen ; Adrien Ugon. IOS Press, 2018. pp. 531-535 (Studies in Health Technology and Informatics).

@inproceedings{f14e1902c14d4a76a1db272437fd3144,

title = "A knowledge-base for a personalized infectious disease risk prediction system",

abstract = "We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.",

keywords = "Infectious disease, Knowledge-base, Ontology, Risk, Rules",

author = "Retno Vinarti and Lucy Hederman",

note = "Publisher Copyright: {\textcopyright} 2018 European Federation for Medical Informatics (EFMI) and IOS Press.; 40th Medical Informatics in Europe Conference, MIE 2018 ; Conference date: 24-04-2018 Through 26-04-2018",

year = "2018",

doi = "10.3233/978-1-61499-852-5-531",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

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Vinarti, R & Hederman, L 2018, A knowledge-base for a personalized infectious disease risk prediction system. in GO Klein, D Karlsson, A Moen & A Ugon (eds), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. Studies in Health Technology and Informatics, vol. 247, IOS Press, pp. 531-535, 40th Medical Informatics in Europe Conference, MIE 2018, Gothenburg, Sweden, 24/04/18. https://doi.org/10.3233/978-1-61499-852-5-531

A knowledge-base for a personalized infectious disease risk prediction system. / Vinarti, Retno; Hederman, Lucy.
Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. ed. / Gunnar O. Klein; Daniel Karlsson; Anne Moen; Adrien Ugon. IOS Press, 2018. p. 531-535 (Studies in Health Technology and Informatics; Vol. 247).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Vinarti, Retno

AU - Hederman, Lucy

PY - 2018

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N2 - We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

AB - We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

KW - Infectious disease

KW - Knowledge-base

KW - Ontology

KW - Risk

KW - Rules

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M3 - Conference contribution

C2 - 29678017

AN - SCOPUS:85046535919

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BT - Building Continents of Knowledge in Oceans of Data

A2 - Klein, Gunnar O.

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PB - IOS Press

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Y2 - 24 April 2018 through 26 April 2018

ER -

Vinarti R, Hederman L. A knowledge-base for a personalized infectious disease risk prediction system. In Klein GO, Karlsson D, Moen A, Ugon A, editors, Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. IOS Press. 2018. p. 531-535. (Studies in Health Technology and Informatics). doi: 10.3233/978-1-61499-852-5-531

A knowledge-base for a personalized infectious disease risk prediction system

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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