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Journal on Data Semantics

, Volume 4, Issue 2, pp 133–147 | Cite as

Ontology-Based Information Extraction: Identifying Eligible Patients for Clinical Trials in Neurology

  • Peter GeibelEmail author
  • Martin Trautwein
  • Hebun Erdur
  • Lothar Zimmermann
  • Kati Jegzentis
  • Michaela Bengner
  • Christian Hans Nolte
  • Thomas Tolxdorff
Original Article

Abstract

In this paper, we present a case study in using ontologies within a system for identifying patients who are eligible for clinical trials. The main purpose of this clinical research data warehouse (CRDW) is to support patient recruitment based on routine data from the clinical information system. In contrast to most other systems for similar purposes, the CRDW also makes use of information extracted from clinical documents like admission reports, radiological findings and discharge letters. The so-called linguistic pipeline of the CDRW recognizes negated and coordinated phrases. It is supported by clinical application ontologies, which enable the identification of main terms and their properties, as well as semantic search with synonyms, hypernyms, and syntactic variants. In the paper, we discuss questions related to designing the ontologies and filling them with content. The CRDW is currently being tested at several departments of the Charité—Universitätsmedizin Berlin and the Vivantes—Netzwerk für Gesundheit GmbH. In the article, we provide a thorough evaluation of the deployed systems based on real data related to clinical trials conducted by our neurology departments.

Keywords

Ontologies Information extraction RDFS eHealth Patient identification Clinical data warehouse Clinical trials 

Notes

Acknowledgments

The project was partially funded by TSB Technologiestiftung Berlin, Zukunftsfonds Berlin, and co-financed by the European Union—European Fund for Regional Development. We would like to thank all computer scientists, linguists, ontologists, physicians, study nurses, and administrative staff of all three partners, who participated in the organization of the project and development of the software.

References

  1. 1.
    Bodenreider O (2004) The unified medical language system (umls): integrating biomedical terminology. Nucl Acids Res 32:267–270 (Database-Issue)CrossRefGoogle Scholar
  2. 2.
    Brinkman RR, Courtot M, Derom D, Fostel JM, He Y, Lord P, Malone J, Parkinson H, Peters B, Rocca-Serra P et al (2010) Modeling biomedical experimental processes with obi. J Biomed Semant 1(Suppl 1):S7CrossRefGoogle Scholar
  3. 3.
    Broekstra J, Kampman A, Van Harmelen F (2002) Sesame: a generic architecture for storing and querying rdf and rdf schema. In: The Semantic Web-ISWC 2002, Springer, Berlin, p 54–68Google Scholar
  4. 4.
    Browne P (2009) Jboss drools business rules. From technologies to solutions, Packt Publishing Limited, India, http://books.google.de/books?id=aweMMi7PttwC
  5. 5.
    Chinchuluun A, Pardalos P, Migdalas A, Pitsoulis L (2008) Pareto optimality, game theory and equilibria, vol 17. Springer, New YorkGoogle Scholar
  6. 6.
    Cowie J, Wilks Y (2000) Information extraction. In: Dale R, Moisl H, Somers H (eds) Handbook of natural language processing. CRC Press, Boca Raton, Florida, USA, pp 241–260Google Scholar
  7. 7.
    Cunningham H, Tablan V, Roberts A, Bontcheva K (2013) Getting more out of biomedical documents with GATE’s full lifecycle open source text analytics. PLoS Comput Biol 9(2):e1002854 doi: 10.1371/journal.pcbi.1002854
  8. 8.
    Dugas M, Lange M, Berdel W, Müller-Tidow C (2008) Workflow to improve patient recruitment for clinical trials within hospital information systems: a case-study. Trials 9(1):2CrossRefGoogle Scholar
  9. 9.
    Gallaire H, Minker J, Nicolas JM (1984) Logic and databases: a deductive approach. ACM Comput Surv 16(2):153–185CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Glock J, Herold R, Pommerening K (2006) Personal identifiers in medical research networks: evaluation of the personal identifier generator in the competence network paediatric oncology and haematology. Submitted for publictionGoogle Scholar
  11. 11.
    Graubner B (2007) ICD und OPS. Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz 50(7):932–943CrossRefGoogle Scholar
  12. 12.
    Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: a semantic web rule language combining OWL and RuleML, vol 21. W3C Member submission. W3C, p 79. http://www.w3.org/Submission/SWRL/.
  13. 13.
    Hripcsak G, Ludemann P, Pryor TA, Wigertz OB, Clayton PD (1994) Rationale for the Arden syntax. Comput Biomed Res 27(4):291–324CrossRefGoogle Scholar
  14. 14.
    Hussain S, Ouagne D, Sadou E, Dart T, Jaulent MC, Vloed BD, Colaert D, Daniel C (2012) EHR4CR: a semantic web based interoperability approach for reusing electronic healthcare records in protocol feasibility studies. In: Paschke A, Burger A, Romano P, Marshall MS, Splendiani A (eds) SWAT4LS, CEUR-WS.org, CEUR Workshop Proceedings, vol 952Google Scholar
  15. 15.
    Jurafsky D, Martin JH (2008) Speech and language processing, 2nd edn., Series in artificial intelligence. Prentice Hall, Upper Saddle River, New Jersey, USAGoogle Scholar
  16. 16.
    Kifer M (2008) Rule interchange format: the framework. Web reasoning and rule systems. In: Calvanese D, Lausen G (eds) LNCS, vol 5341. Springer, Berlin Heidelberg, Germany, pp 1–11Google Scholar
  17. 17.
    Kifer M, Lausen G, Wu J (1995) Logical foundations of object-oriented and frame-based languages. J ACM 42(4):741–843CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Lloyd JW (1987) Foundations of logic programming, 2nd edn. Springer-Verlag New York, Inc. New York, USAGoogle Scholar
  19. 19.
    Lo O, Fan L, Buchanan WJ, Thuemmler C (2012) Technical evaluation of an e-health platform. In: Macedo M (ed) IADIS E-Health, IADIS, pp 21–28. ISBN 978-972-8939-70-0 Google Scholar
  20. 20.
    Marwede D, Schulz T, Kahn T (2008) Indexing thoracic CT reports using a preliminary version of a standardized radiological lexicon (radlex). J Digit Imag 21(4):363–370Google Scholar
  21. 21.
    Miles A, Pérez-Agüera JR (2007) SKOS: simple knowledge organisation for the web. Catal Class Quarterly 43(3–4):69–83Google Scholar
  22. 22.
    Müller F (2005) A finite-state approach to shallow parsing and grammatical functions annotation of german. PhD thesis, University of Tubingen, Tübingen, GermanyGoogle Scholar
  23. 23.
    Murphy SN, Mendis ME, Berkowitz DA, Chueh IKH (2006) Integration of clinical and genetic data in the i2b2 architecture. In: AMIA Annu Symp Proc, (2009)Google Scholar
  24. 24.
    Polleres A (2007) From SPARQL to rules (and back). In: Zurko ME, Patel-Schneider PF, Shenoy PJ, Williamson CL (eds.) ACM, USA, p 787–796Google Scholar
  25. 25.
    Rector A, Rogers J, Zanstra P, Van Der Haring E (2003) OpenGALEN: open source medical terminology and tools. In: AMIA Annual Symposium Proceedings, American Medical Informatics Association, vol 2003, p 982Google Scholar
  26. 26.
    Reeve L (2005) Survey of semantic annotation platforms. In: Proceedings of the 2005 ACM Symposium on Applied Computing, ACM Press, USA, p 1634–1638Google Scholar
  27. 27.
    Rogers FB (1963) Medical subject headings. Bull Med Libr Assoc 51:114–116Google Scholar
  28. 28.
    Rosse C, Mejino J (2003) A reference ontology for biomedical informatics: the foundational model of anatomy. J Biomed Inform 36:478–500CrossRefGoogle Scholar
  29. 29.
    Ruch P, Gobeill J, Lovis C, Geissbuhler A (2008) Automatic medical encoding with SNOMED categories. BMC Med Inform Dec Making 8:6CrossRefGoogle Scholar
  30. 30.
    Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentic Hall, Upper Saddle River, New Jersey, USAGoogle Scholar
  31. 31.
    Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Schuler KK, Chute CG (2010) Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. JAMIA 17(5):507–513Google Scholar
  32. 32.
    Scheitz JF, Mochmann HC, Nolte CH, Haeusler KG, Audebert HJ, Heuschmann PU, Laufs U, Witzenbichler B, Schultheiss HP, Endres M (2011) Troponin elevation in acute ischemic stroke (TRELAS)—protocol of a prospective observational trial. M BMC Neurol 11:98CrossRefGoogle Scholar
  33. 33.
    Scheitz JF, Mochmann HC, Fiebach BWB, Audebert HJ, Nolte CH (2012) J Neurol 259(1):188–190Google Scholar
  34. 34.
    Shvaiko P, Euzenat J (2013) Ontology matching: state of the art and future challenges. Knowledge and Data Engineering, IEEE Transactions on , vol 25, issue 1. IEEE Press, Piscataway, NJ, USAGoogle Scholar
  35. 35.
    Staab S, Studer R (2009) Handbook on ontologies, 2nd edn. Springer, Berlin Heidelberg, GermanyGoogle Scholar
  36. 36.
    Szarvas G, Farkas R, Busa-Fekete R (2007) Research paper: state-of-the-art anonymization of medical records using an iterative machine learning framework. JAMIA 14(5):574–580Google Scholar
  37. 37.
    Todorov K, Geibel P, Kuhnberger KU (2010) Mining concept similarities for heterogeneous ontologies. In: Perner P (ed) Advances in data mining. Applications and theoretical aspects, vol 6171. Springer, Berlin, pp 86–100CrossRefGoogle Scholar
  38. 38.
    Wimalasuriya DC, Dou D (2010) Ontology-based information extraction: an introduction and a survey of current approaches. J Info Sci 36(3):306–323CrossRefGoogle Scholar
  39. 39.
    World Health Organization (2004) ICD-10: International statistical classification of diseases and related health problems. World Health Organization, Geneva, SwitzerlandGoogle Scholar
  40. 40.
    Yu L (2011) A developers guide the semantic web. Springer, Berlin Heidelberg, GermanyGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Peter Geibel
    • 1
    Email author
  • Martin Trautwein
    • 4
  • Hebun Erdur
    • 2
  • Lothar Zimmermann
    • 1
  • Kati Jegzentis
    • 3
  • Michaela Bengner
    • 5
  • Christian Hans Nolte
    • 2
  • Thomas Tolxdorff
    • 1
  1. 1.Institute of Medical InformaticsCharité—Universitätsmedizin BerlinBerlinGermany
  2. 2.Department of Neurology (CBF)Charité—Universitätsmedizin BerlinBerlinGermany
  3. 3.Center for Stroke Research Berlin (CSB)Charité—Universitätsmedizin Berlin, Campus MitteBerlinGermany
  4. 4.Vivantes—Netzwerk für Gesundheit GmbHBerlinGermany
  5. 5.Klinik für NeurologieVivantes—Netzwerk für Gesundheit GmbHBerlinGermany

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