Semantic similarity-driven decision support in the skeletal dysplasia domain

Authors:
Razan Paul;Tudor Groza;Andreas Zankl;Jane Hunter
Affiliations:
School of ITEE, The University of Queensland, Australia;School of ITEE, The University of Queensland, Australia;Bone Dysplasia Research Group, UQ Centre for Clinical Research (UQCCR), The University of Queensland, Australia,Genetic Health Queensland, Royal Brisbane and Women's Hospital, Herston, Australia;School of ITEE, The University of Queensland, Australia
Venue:
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Year:
2012

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Abstract

Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain.