This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain. We investigate whether it is possible to use such reasoning to automate common clinical tasks that are currently labor intensive and error prone, and focus our case study on improving cohort selection for clinical trials. An obstacle to automating such clinical tasks is the need to bridge the semantic gulf between raw patient data, such as laboratory tests or specific medications, and the way a clinician interprets this data. Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval. We describe the technical challenges to building a realistic case study, which include problems related to scalability, the integration of large ontologies, and dealing with noisy, inconsistent data. Our solution is based on the SNOMED CT ontology, and scales to one year of patient records (approx. 240,000 patients).

@inproceedings{Srinivas/2007/Matching, author = {Srinivas, Kavitha and Patel, Chintan and Cimino, James and Ma, Li and Dolby, Julian and Fokoue, Achille and Kalyanpur, Aditya and Kershenbaum, Aaron and Schonberg, Edith}, title = {Matching Patient Records to Clinical Trials Using Ontologies}, editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe}, booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea}, series = {LNCS}, publisher = {Springer Verlag}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4825}, pages = {809--822}, url = {http://iswc2007.semanticweb.org/papers/809.pdf}, keywords = {2007 clinical health in_use_2 iswc matching ontology ontology_(computer_science) patient pharmaceuticals record semantic_web trial using}, abstract = {This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain. We investigate whether it is possible to use such reasoning to automate common clinical tasks that are currently labor intensive and error prone, and focus our case study on improving cohort selection for clinical trials. An obstacle to automating such clinical tasks is the need to bridge the semantic gulf between raw patient data, such as laboratory tests or specific medications, and the way a clinician interprets this data. Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval. We describe the technical challenges to building a realistic case study, which include problems related to scalability, the integration of large ontologies, and dealing with noisy, inconsistent data. Our solution is based on the SNOMED CT ontology, and scales to one year of patient records (approx. 240,000 patients).} }

%0 = inproceedings %A = Srinivas, Kavitha and Patel, Chintan and Cimino, James and Ma, Li and Dolby, Julian and Fokoue, Achille and Kalyanpur, Aditya and Kershenbaum, Aaron and Schonberg, Edith %B = Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea %C = Berlin, Heidelberg %D = 2007 %I = Springer Verlag %T = Matching Patient Records to Clinical Trials Using Ontologies %U = http://iswc2007.semanticweb.org/papers/809.pdf