Abstract
Ontology is a set of concepts in a domain that shows their properties and the relations between them. Medical domain Ontology is widely used and very popular in e-healthcare, medical information systems, etc. The most significant benefit that Ontology may bring to healthcare systems is its ability to support the indispensable integration of knowledge and data (Pisanelli et al, Proceedings biological and medical data analysis, 6th international symposium, 2005, [1]). Graph structure is very important tool for Foundation, Analysis, and Domain Knowledge. Ontology as a graphical model envisages the process of any system and present appropriate analysis (Pedrinaci, Ontology-based metrics computation for business process analysis, [2]). In this study, the knowledge provided by the Ontology is further explored to obtain the related concepts. An algorithm to compute the related concepts of Ontology is also proposed in a simplified manner using Boolean Matrix. The inferences from this study may serve to improve the diagnosis process in the field of Biomedical Intelligence and Clinical Data Analysis.
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Parveen, S., Biswas, R. (2019). A Clinical Data Analytic Metric for Medical Ontology Using Semantic Similarity. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_46
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DOI: https://doi.org/10.1007/978-3-030-00665-5_46
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