A Survey on Ontologies and Ontology Alignment Approaches in Healthcare

Dimitrieski, Vladimir; Petrovic, Gajo; Kovačević, Aleksandar; Luković, Ivan; Fujita, Hamido

doi:10.1007/978-3-319-42007-3_32

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Ontologies have been successfully applied in different domains either as a stand-alone standardization effort or as part of semantic web applications, such as education [e.g., Stancin et al (2020)], behavior [e.g., Blanch et al (2017)], behavior change [e.g., Norris et al (2019)], affective research [e.g., Abaalkhail et al (2018)], and healthcare [e.g., Dimitrieski et al (2016)]. Ontologies provide structured annotations to diverse and seemingly non-compatible data and enable the analysis of raw data in an automated way while still being flexible to accommodate different theories and compare different findings.…”

Section: Background: Ontologiesmentioning

confidence: 99%

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

2021

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BackgroundEspecially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being.MethodsThis work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts.ResultsThe already existing CCOnto was modified to automatically categorize university students’ state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization.DiscussionThe paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.

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Section: Background: Ontologiesmentioning

confidence: 99%

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

2021

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“…Moreover, a large number of medical specific codes can make it hard to overcome potential coding discrepancies. Several works in the literature have used ontology-based approaches for health data integration [17,30]. However, only few works include the modeling of interactions, negotiation, and collaboration among intelligent and autonomous systems [11], as in τ Agents.…”

Section: Ontology Agreementmentioning

confidence: 99%

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

2020

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Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions-in the form of mobile apps and wearables-have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support. Keywords Patient trajectories • Semantic modeling • Agent-based modeling This article is part of the Topical Collection on Healthcare Intelligent Multi-Agent Systems (HIMAS2020)

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“…Such method allows the knowledge granularity to be analyzed between ontologies and facilitates several ontology engineering techniques. The readers can look into the work of Dimitrieski et al 13 for more details and critical reviews of healthcare ontologies and existing ontology alignment approaches. However, in most cases (such as commercial organization, etc), the ontology alignment approaches rely on the third parties 10 .…”

Section: Related Workmentioning

confidence: 99%

Ontology negotiation: Knowledge interchange between distributed ontologies through agent negotiation

Zhu

Teng

et al. 2019

Concurrency and Computation

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Summary With the proliferation of knowledge source on the internet as well as the widely professional agents, the knowledge interchange is drawing much attention. Ontology is recognized as the crucial technology due to their nature of sharing, formalization, and conceptualization to integrate and share the knowledge. In this paper, by interpreting and negotiating the communication content, a unified understanding of knowledge is formed; then, we can realize the interoperability between ontologies. We have developed the ontology automatic negotiation by agent elect protocol (AEP) to elect optimal participants and encourage agents to obey the protocol, concept mapping protocol (CMP) to find the corresponding concept mappings with the highest relevancy, and in addition, agent negotiation protocol (ANP). In ANP, we define the simultaneous negotiation protocol and agents' strategies to combine distributed ontologies interchange with agent negotiation. Finally, the implementation and preliminary results are given to verify the validity of the proposed ontology negotiation.

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A Survey on Ontologies and Ontology Alignment Approaches in Healthcare

Cited by 6 publications

References 19 publications

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

Ontology negotiation: Knowledge interchange between distributed ontologies through agent negotiation

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