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Review

Survey on Knowledge Representation Models in Healthcare

by
Batoul Msheik
1,*,
Mehdi Adda
2,
Hamid Mcheick
2 and
Mohamed Dbouk
3
1
Computer Science Department, Université du Québec à Chicoutimi, 555, Boul De l’Université, Chicoutimi, QC G7H 2B1, Canada
2
Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
3
Computer Science Department, Université Libanaise, Hadath, Beirut 6573/14, Lebanon
*
Author to whom correspondence should be addressed.
Information 2024, 15(8), 435; https://doi.org/10.3390/info15080435
Submission received: 9 April 2024 / Revised: 17 June 2024 / Accepted: 25 June 2024 / Published: 26 July 2024
(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)

Abstract

Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction.
Keywords: knowledge representation model; healthcare requirements; heterogeneity; interpretability; scalability and reasoning knowledge representation model; healthcare requirements; heterogeneity; interpretability; scalability and reasoning

Share and Cite

MDPI and ACS Style

Msheik, B.; Adda, M.; Mcheick, H.; Dbouk, M. Survey on Knowledge Representation Models in Healthcare. Information 2024, 15, 435. https://doi.org/10.3390/info15080435

AMA Style

Msheik B, Adda M, Mcheick H, Dbouk M. Survey on Knowledge Representation Models in Healthcare. Information. 2024; 15(8):435. https://doi.org/10.3390/info15080435

Chicago/Turabian Style

Msheik, Batoul, Mehdi Adda, Hamid Mcheick, and Mohamed Dbouk. 2024. "Survey on Knowledge Representation Models in Healthcare" Information 15, no. 8: 435. https://doi.org/10.3390/info15080435

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