Abstract
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.
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References
TIES-Text Information Extraction System (2017). http://ties.dbmi.pitt.edu/
Abedi, V., Zand, R., Yeasin, M., Faisal, F.E.: An automated framework for hypotheses generation using literature. BioData Min. 5(1), 13 (2012)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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)
Campos, D., et al.: Harmonization of gene/protein annotations: towards a gold standard MEDLINE. Bioinformatics 28(9), 1253–1261 (2012)
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). https://doi.org/10.1007/978-3-319-46523-4_9
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). https://doi.org/10.1007/978-3-319-21843-4_5
Dai, M., et al.: An efficient solution for mapping free text to ontology terms. In: AMIA Summit on Translational Bioinformatics, vol. 21 (2008)
Dugas, M., et al.: Portal of medical data models: information infrastructure for medical research and healthcare. Database: J. Biol. Databases Curation (2016)
Köpcke, H., Thor, A., Rahm, E.: Learning-based approaches for matching web data entities. IEEE Internet Comput. 14(4), 23–31 (2010)
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). https://doi.org/10.1007/978-3-319-69751-2_1
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)
Tanenblatt, M.A., Coden, A., Sominsky, I.L.: The ConceptMapper approach to named entity recognition. In: Proceedings of LREC, pp. 546–551 (2010)
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)
Zou, Q., et al.: IndexFinder: a knowledge-based method for indexing clinical texts. In: Proceedings of AMIA Annual Symposium, pp. 763–767 (2003)
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Christen, V. et al. (2019). A Learning-Based Approach to Combine Medical Annotation Results. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_13
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DOI: https://doi.org/10.1007/978-3-030-06016-9_13
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