A Learning-Based Approach to Combine Medical Annotation Results

(Short Paper)
  • Victor ChristenEmail author
  • Ying-Chi Lin
  • Anika Groß
  • Silvio Domingos Cardoso
  • Cédric Pruski
  • Marcos Da Silveira
  • Erhard Rahm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)


There exist many tools to annotate mentions of medical entities in documents with concepts from biomedical ontologies. To improve the overall quality of the annotation process, we propose the use of machine learning to combine the results of different annotation tools. We comparatively evaluate the results of the machine-learning based approach with the results of the single tools and a simpler set-based result combination.


Biomedical annotation Annotation tool Machine learning 


  1. 1.
    TIES-Text Information Extraction System (2017).
  2. 2.
    Abedi, V., Zand, R., Yeasin, M., Faisal, F.E.: An automated framework for hypotheses generation using literature. BioData Min. 5(1), 13 (2012)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  4. 4.
    Campos, D., Matos, S., Oliveira, J.: Current methodologies for biomedical named entity recognition. In: Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, pp. 839–868 (2013)CrossRefGoogle Scholar
  5. 5.
    Campos, D., et al.: Harmonization of gene/protein annotations: towards a gold standard MEDLINE. Bioinformatics 28(9), 1253–1261 (2012)CrossRefGoogle Scholar
  6. 6.
    Christen, V., Groß, A., Rahm, E.: A reuse-based annotation approach for medical documents. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 135–150. Springer, Cham (2016). Scholar
  7. 7.
    Christen, V., Groß, A., Varghese, J., Dugas, M., Rahm, E.: Annotating medical forms using UMLS. In: Ashish, N., Ambite, J.-L. (eds.) DILS 2015. LNCS, vol. 9162, pp. 55–69. Springer, Cham (2015). Scholar
  8. 8.
    Dai, M., et al.: An efficient solution for mapping free text to ontology terms. In: AMIA Summit on Translational Bioinformatics, vol. 21 (2008)Google Scholar
  9. 9.
    Dugas, M., et al.: Portal of medical data models: information infrastructure for medical research and healthcare. Database: J. Biol. Databases Curation (2016)Google Scholar
  10. 10.
    Köpcke, H., Thor, A., Rahm, E.: Learning-based approaches for matching web data entities. IEEE Internet Comput. 14(4), 23–31 (2010)CrossRefGoogle Scholar
  11. 11.
    Lin, Y.-C., et al.: Evaluating and improving annotation tools for medical forms. In: Da Silveira, M., Pruski, C., Schneider, R. (eds.) DILS 2017. LNCS, vol. 10649, pp. 1–16. Springer, Cham (2017). Scholar
  12. 12.
    Savova, G.K., et al.: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. JAMIA 17(5), 507–513 (2010)Google Scholar
  13. 13.
    Tanenblatt, M.A., Coden, A., Sominsky, I.L.: The ConceptMapper approach to named entity recognition. In: Proceedings of LREC, pp. 546–551 (2010)Google Scholar
  14. 14.
    Tseytlin, E., Mitchell, K., Legowski, E., Corrigan, J., Chavan, G., Jacobson, R.S.: NOBLE-Flexible concept recognition for large-scale biomedical natural language processing. BMC Bioinform. 17(1), 32 (2016)CrossRefGoogle Scholar
  15. 15.
    Zou, Q., et al.: IndexFinder: a knowledge-based method for indexing clinical texts. In: Proceedings of AMIA Annual Symposium, pp. 763–767 (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Victor Christen
    • 1
    Email author
  • Ying-Chi Lin
    • 1
  • Anika Groß
    • 1
  • Silvio Domingos Cardoso
    • 2
    • 3
  • Cédric Pruski
    • 2
  • Marcos Da Silveira
    • 2
  • Erhard Rahm
    • 1
  1. 1.University of LeipzigLeipzigGermany
  2. 2.LIST, Luxembourg Institute of Science and TechnologyEsch-sur-AlzetteLuxembourg
  3. 3.LRI, University of Paris-Sud XIGif-sur-YvetteFrance

Personalised recommendations