Anzeige
Erschienen in:
2007 | OriginalPaper | Buchkapitel
Matching Patient Records to Clinical Trials Using Ontologies
verfasst von : Chintan Patel, James Cimino, Julian Dolby, Achille Fokoue, Aditya Kalyanpur, Aaron Kershenbaum, Li Ma, Edith Schonberg, Kavitha Srinivas
Erschienen in: The Semantic Web
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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).