A Guiding Framework for Ontology Reuse in the Biomedical Domain

Shah, Tejal; Rabhi, Fethi A.; Ray, Pradeep; Taylor, Kerry

doi:10.1109/hicss.2014.360

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…Finally, [ 30 ] focused on answering the question of “how the SW tools such as ontologies and rules can be applied to connect the medical and oral health (M-OH) domains by developing a KB.” The KB can be reused by the medical information systems for semantic interoperability and reasoning process. The system has been developed by utilizing OWL and SWRL rules.…”

Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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BackgroundKnowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.ObjectiveThe two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.MethodsWe conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.ResultsThe retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.ConclusionsThe potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

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Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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“…Shah et al constructed a framework based on MetaMap and SemRep to reuse terms from SNOMED-CT ontology. They applied the framework to construct an ontology that combines the dental and medical domain to allow better reasoning over common knowledge [20].…”

Section: Related Workmentioning

confidence: 99%

PreMedOnto: A Computer Assisted Ontology for Precision Medicine

Tawfik

Spruit

2019

Natural Language Processing and Information Systems

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This paper proposes an ontology learning framework that combines text mining, information extraction and retrieval. The proposed model takes advantage of existing structured knowledge by reusing terms and concepts from other ontologies. We further apply the methodology to create a detailed ontology for the emerging precision medicine (PM) domain by collecting a corpus of relevant articles and mapping its frequent terms to existing concepts. The resulting ontology consists of 543 annotated classes. The ontology was also tested for effectiveness by applying two evaluation frameworks to validate its design and quality. The results demonstrate that the ontology learning system is able to capture and represent the semantics of the PM domain with high precision and significance. Moreover, the computer-assisted construction process reduced dependency on expert knowledge. The developed PreMedOnto ontology could be further used to enhance the potentials of other NLP applications in the PM domain.

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“…The system should effectively and efficiently analyse, integrate and interpret knowledge to be used by the user in enhancing treatment outcomes and patient health (Holzinger and Jurisica, 2014). Due to information silos, which fragment the medical and dental domains (Shah et al, 2014), it is important that data from both domains is seamlessly integrated for efficient processing and distribution to clinicians. Besides early medical prognosis (as many medical conditions are manifested first in oral cavity), other benefits of medical and dental record integration are (Rudman et al, 2010):…”

Section: Big Datamentioning

confidence: 99%

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

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A Guiding Framework for Ontology Reuse in the Biomedical Domain

Cited by 11 publications

References 26 publications

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

PreMedOnto: A Computer Assisted Ontology for Precision Medicine

Decision support systems for adoption in dental clinics: A survey

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