Advertisement

Matching Patient Records to Clinical Trials Using Ontologies

  • Chintan Patel
  • James Cimino
  • Julian Dolby
  • Achille Fokoue
  • Aditya Kalyanpur
  • Aaron Kershenbaum
  • Li Ma
  • Edith Schonberg
  • Kavitha Srinivas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4825)

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).

Keywords

Semantic Retrieval Closed World Assumption Patient Dataset Ontology Reasoning Radiology Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Simes, R.: Clinical trials and real-world medicine. Trial evidence best informs real-world medicine when it is relevant to the clinical problem. Med. J.Aust. 177(8), 410–411 (2002)Google Scholar
  2. 2.
    Embi, P., Jain, A., Clark, J., Bizjack, S., Hornung, R., Harris, C.: Effect of a clinical trial alert system on physician participation in trial recruitment. Arch.Intern.Med. 165(19), 2272–2277 (2005)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Clinical Trials, http://clinicaltrials.gov/
  5. 5.
    Noy, N.F.: Semantic integration: a survey of ontology-based approachesGoogle Scholar
  6. 6.
    Horrocks, I.: Using an expressive descriptive logic:fact or fiction? In: KR 1998. Proceedings of the 6th Int. Conf. on principles of Knowledge Representation and Reasoning, pp. 636–647 (1998)Google Scholar
  7. 7.
    Sirin, E., Parsia, B.: Pellet: An owl dl reasoner. In: Description Logics (2004)Google Scholar
  8. 8.
    Haarslev, V., Moller, R.: Racer system description. In: Goré, R.P., Leitsch, A., Nipkow, T. (eds.) IJCAR 2001. LNCS (LNAI), vol. 2083, pp. 701–705. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Hustadt, U., Motik, B., Sattler, U.: Reducing shiq description logic to disjunctive datalog programs. In: KR 2004. Proc. of 9th Intl. Conf. on Knowledge Representation and Reasoning, pp. 152–162 (2004)Google Scholar
  10. 10.
    Dolby, J., Fokoue, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K.: Scalable semantic retrieval through summarization and refinement. In: Proc. of the AAAI Conf. (2007)Google Scholar
  11. 11.
    Baader, F.: Brandt, S., Lutz, C.: Pushing the el envelope. Technical report, Chair of Automata Theory, Institure for Theoretical Computer Science, Dresden University of Technology (2005)Google Scholar
  12. 12.
    Fokoue, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K.: The summary abox:cutting ontologies down to size. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 136–145. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Cimino, J., Clayton, P., Hripcsak, G., Johnson, S.: Knowledge-based approaches to the maintenance of a large controlled medical terminology. J. Am Med. Inform. Assoc. 1(1), 35–50 (1994)Google Scholar
  14. 14.
    Johnson, S.: Generic data modeling for clinical repositories. J. Am Med. Inform. Assoc. 3(5), 328–339 (1996)Google Scholar
  15. 15.
    DA, D.L., Humphreys, B., McCray, A.: The unified medical language system. Methods Inf. Med. 32(4), 281–291 (1993)Google Scholar
  16. 16.
  17. 17.
    Grimm, S., Motik, B.: Closed world reasoning in the semantic web through epistemic operators. In: OWLED 2005. Proc. of the Workshop on OWL: Experiences and Directions (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chintan Patel
    • 3
  • James Cimino
    • 3
  • Julian Dolby
    • 1
  • Achille Fokoue
    • 1
  • Aditya Kalyanpur
    • 1
  • Aaron Kershenbaum
    • 1
  • Li Ma
    • 2
  • Edith Schonberg
    • 1
  • Kavitha Srinivas
    • 1
  1. 1.IBM Watson Research Center,P.O.Box 704, Yorktown Heights, NY 10598USA
  2. 2.IBM China Research Lab, Beijing 100094China
  3. 3.Columbia University Medical Center 

Personalised recommendations