Knowledge representation for infectious disease risk prediction system: A literature review

Retno Aulia Vinarti

doi:10.1016/j.procs.2019.11.188

Knowledge representation for infectious disease risk prediction system: A literature review

Retno Aulia Vinarti^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

This article contains a literature review to seek knowledge representation for infectious disease risk prediction system. The knowledge representation should be able to encode knowledge related to infectious diseases and usable by experts. 188 articles are collected through several constraints. From these articles, 14 knowledge representations are gathered. Ontology comes out as the most used knowledge representation related to disease, followed by fuzzy and rules. This leads to the next step of the research, on how to encode knowledge using these representations.

Original language	English
Pages (from-to)	821-825
Number of pages	5
Journal	Procedia Computer Science
Volume	161
DOIs	https://doi.org/10.1016/j.procs.2019.11.188
Publication status	Published - 2019
Event	5th Information Systems International Conference, ISICO 2019 - Surabaya, Indonesia Duration: 23 Jul 2019 → 24 Jul 2019

Keywords

Infectious diseases
Knowledge representation
Prediction

UN SDGs

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

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10.1016/j.procs.2019.11.188

Cite this

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title = "Knowledge representation for infectious disease risk prediction system: A literature review",

abstract = "This article contains a literature review to seek knowledge representation for infectious disease risk prediction system. The knowledge representation should be able to encode knowledge related to infectious diseases and usable by experts. 188 articles are collected through several constraints. From these articles, 14 knowledge representations are gathered. Ontology comes out as the most used knowledge representation related to disease, followed by fuzzy and rules. This leads to the next step of the research, on how to encode knowledge using these representations.",

keywords = "Infectious diseases, Knowledge representation, Prediction",

author = "Vinarti, {Retno Aulia}",

note = "Publisher Copyright: {\textcopyright} 2019 The Authors.; 5th Information Systems International Conference, ISICO 2019 ; Conference date: 23-07-2019 Through 24-07-2019",

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AB - This article contains a literature review to seek knowledge representation for infectious disease risk prediction system. The knowledge representation should be able to encode knowledge related to infectious diseases and usable by experts. 188 articles are collected through several constraints. From these articles, 14 knowledge representations are gathered. Ontology comes out as the most used knowledge representation related to disease, followed by fuzzy and rules. This leads to the next step of the research, on how to encode knowledge using these representations.

KW - Infectious diseases

KW - Knowledge representation

KW - Prediction

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U2 - 10.1016/j.procs.2019.11.188

DO - 10.1016/j.procs.2019.11.188

M3 - Conference article

AN - SCOPUS:85078895779

SN - 1877-0509

VL - 161

SP - 821

EP - 825

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 23 July 2019 through 24 July 2019

ER -

Knowledge representation for infectious disease risk prediction system: A literature review

Abstract

Keywords

UN SDGs

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