Matching Patient Records to Clinical Trials Using Ontologies
A paper written by Achille Fokoue, Li Ma, Aaron Kershenbaum, Julian Dolby, Chintan Patel, James Cimino, Aditya Kalyanpur, Kavitha Srinivas and Edith Schonberg. It was presented at the ISWC2007+ASWC2007.
[edit] 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).
A linked list of all papers is provided in the article on ISWC2007+ASWC2007 papers. This article has originally been created from the ISWC 2007/ASWC 2007 metadata.
