Authors:
Krystyna Milian
1
;
Anca Bucur
2
;
Frank van Harmelen
3
and
Annette ten Teije
4
Affiliations:
1
VU University Amsterdam and Philips Research Eindhoven, Netherlands
;
2
Philips Research Eindhoven, Netherlands
;
3
VU University Amsterdam, Netherlands
;
4
VU University of Amsterdam, Netherlands
Keyword(s):
Semantic Analysis, Selecting Ontology Subsets, Concepts Relevance, Ontology Annotators, Medical Ontologies.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Health Information Systems
;
Semantic Interoperability
Abstract:
Since eligibility criteria of clinical trials are represented as free text, their automatic interpretation and the evaluation of patient eligibility is challenging. Our approach to the criteria processing is based on the identification of contextual patterns and semantic concepts that together define the machine-interpretable meaning. The goal of this research is to find the most relevant concepts occurring in eligibility criteria that need to be mapped to patient record to enable automatic evaluation of patient eligibility. Based on the analysis of annotation of breast cancer trials obtained using different concept recognizers and ontologies from UMLS Thesaurus, we chose to use MetaMap and SNOMED CT to create the mapping set. To prioritize the identified concepts, we used the tf-idf measure and the corpus of over 38, 000 various clinical trials, to detect concepts specific for breast cancer, and cancer in general. The obtained results can guide the mapping order of criteria concepts
to patient data. The observed substantial overlap between the terms occurring in criteria from the trials related to breast cancer and other diseases will reduce the cost of extending the trial matching system to other diseases.
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