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Matching Patient Records to Clinical Trials Using Ontologies

by: Kavitha Srinivas and Chintan Patel and James Cimino and Li Ma and Julian Dolby and Achille Fokoue and Aditya Kalyanpur and Aaron Kershenbaum and Edith Schonberg

In: Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference ISWC/ASWC2007, Busan, South Korea , Vol. 4825Berlin, Heidelberg: Springer Verlag , November (2007) , p. 809--822.

Resources (URL, PDF, PS...)

URL:	http://iswc2007.semanticweb.org/papers/809.pdf

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.

BibTeX record

@inproceedings{Srinivas/2007/Matching,
  address = {Berlin, Heidelberg},
  author = {Kavitha Srinivas and Chintan Patel and James Cimino and Li Ma and Julian Dolby and Achille Fokoue and Aditya Kalyanpur and Aaron Kershenbaum and Edith Schonberg},
  booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
  crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
  editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
  interHash = {08ca10184b9c16e8ab9de3c9dfea9032},
  intraHash = {4e19935df46a5112bb2497ad87d8a3b4},
  pages = {809--822},
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {Matching Patient Records to Clinical Trials Using Ontologies},
  url = {http://iswc2007.semanticweb.org/papers/809.pdf},
  volume = {4825},
  year = {2007},
  month = {November},
  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).}
}

Endnote record

%0 Conference Paper
%1 Srinivas/2007/Matching
%A Kavitha Srinivas
%A Chintan Patel
%A James Cimino
%A Li Ma
%A Julian Dolby
%A Achille Fokoue
%A Aditya Kalyanpur
%A Aaron Kershenbaum
%A Edith Schonberg
%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
%E Karl Aberer
%E Key-Sun Choi
%E Natasha Noy
%E Dean Allemang
%E Kyung-Il Lee
%E Lyndon J B Nixon
%E Jennifer Golbeck
%E Peter Mika
%E Diana Maynard
%E Guus Schreiber
%E Philippe Cudré-Mauroux
%I Springer Verlag
%K 
%P 809--822
%T Matching Patient Records to Clinical Trials Using Ontologies
%U http://iswc2007.semanticweb.org/papers/809.pdf
%V 4825
%X 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).