2017/05/22 by Darko Katić, Maria Maleshkova, Katić, Darko +23
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #Biomedical Text Mining and Ontologies #Computers and Society (cs.CY) #FOS: Computer and information sciences #Semantic Web and Ontologies #cs.CY
paper · pdf · doi:10.48550/arxiv.1705.07747
4 pages, 1 figure, Surgical Data Science Workshop, Heidelberg, June 20th, 2016
arxiv created 2017/05/22 · openalex publication_date 2017/05/22 · arxiv updated 2017/05/23 · openalex created_date 2017/06/05 · openalex updated_date 2026/07/28
Every year approximately 234 million major surgeries are performed, leading to plentiful, highly diverse data. This is accompanied by a matching number of novel algorithms for the surgical domain. To garner all benefits of surgical data science it is necessary to have an unambiguous, shared understanding of algorithms and data. This includes inputs and outputs of algorithms and thus their function, but also the semantic content, i.e. meaning of data such as patient parameters. We therefore propose the establishment of a new ontology for data and algorithms in surgical data science. Such an ontology can be used to provide common data sets for the community, encouraging sharing of knowledge and comparison of algorithms on common data. We hold that this is a necessary foundation towards new methods for applications such as semantic-based content retrieval and similarity measures and that it is overall vital for the future of surgical data science.