Abstract
Pharmaceutical companies and regulatory authorities are also affected by the current digitalization process and transform their paper-based, document-oriented communication to a structured, digital information exchange. The documents exchanged so far contain a huge amount of information that needs to be transformed into a structured format to enable a more efficient communication in the future. In such a setting, it is important that the information extracted from documents is very accurate as the information is used in a legal, regulatory process and also for the identification of unknown adverse effects of medicinal products that might be a threat to patients’ health. In this paper, we present our layout-aware semi-automatic information extraction system LASIE that combines techniques from rule-based information extraction, flexible data management, and semantic information management in a user-centered design. We applied the system in a case study with an industrial partner and achieved very satisfying results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The example has been taken from http://agence-tst.ansm.sante.fr/html/pdf/3/expor.pdf which is actually an export license of the French authority (ANSM). The manufacturing licenses which we considered in our use case had a similar structure; due to reasons of confidentiality, we cannot show the documents which we processed.
- 2.
- 3.
Optical character recognition.
- 4.
MedDRA® trademark is owned by IFPMA on behalf of ICH. There are other medical terminology systems (or ontologies) available, but we have to use MedDRA® as it is the terminology required by the authorities.
- 5.
- 6.
- 7.
- 8.
The final layout of such documents depend on many factors, including especially the settings of the selected printer. Thus, the layout of a certain page is not stored in the file, but only created when the document is rendered on a screen or printer.
- 9.
References
Aguiar, B.L., Mendes, E., Ferreira, L. Information extraction from medication leaflets. Ph.D. thesis, Master thesis, FEUP, Porto (2012)
Bakiu, B.: Layout-aware semantic information extraction from semi-structured documents. RWTH Aachen University, Master (2015)
Chiticariu, L., Krishnamurthy, R., Li, Y., Raghavan, S., Reiss, F.R., Vaithyanathan, S.: SystemT: an algebraic approach to declarative information extraction. In: Proceeding 48th Annual Meeting Assocation Computational Linguistics, ACL 2010, pp. 128–137. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A., Damljanovic, D., et al.: Developing language processing components with GATE version 7 (a user guide). University of Sheffield, UK (2013). https://gate.ac.uk/sale/tao/index.html
Duke, J.D., Friedlin, J.: ADESSA: a real-time decision support service for delivery of semantically coded adverse drug event data. In: AMIA Annual Symposium Proceedings, vol. 2010, 177–181 (2010)
Ejiri, M.: Knowledge-based approaches to practical image processing. In: Industrial Applications of Machine Intelligence and Vision (MIV-89), Tokyo, 10–12 April 1989, p. 1 (1989)
Gao, L., Tang, Z., Lin, X., Liu, Y., Qiu, R., Wang, Y.: Structure extraction from PDF-based book documents. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital libraries, pp. 11–20. ACM (2011)
Ge, C., Zhang, Y., Duan, H., Li, H.: Identification of adverse drug events in chinese clinical narrative text. In: Park, J.J.J.H., Pan, Y., Chao, H.-C., Yi, G. (eds.) Ubiquitous Computing Application and Wireless Sensor. LNEE, vol. 331, pp. 605–612. Springer, Dordrecht (2015). doi:10.1007/978-94-017-9618-7_62
Gobel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 Table Competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1449–1453. IEEE, August 2013
Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013)
Iqbal, E., Mallah, R., Jackson, R.G., Ball, M., Ibrahim, Z.M., Broadbent, M., Dzahini, O., Stewart, R., Johnston, C., Dobson, R.J.B.: Identification of adverse drug events from free text electronic patient records and information in a large mental health case register. PLoS One 10(8), e0134208 (2015)
Kensche, D., Quix, C., Chatti, M.A., Jarke, M.: GeRoMe: a generic role based metamodel for model management. In: Spaccapietra, S., et al. (eds.) Journal on Data Semantics VIII. LNCS, vol. 4380, pp. 82–117. Springer, Heidelberg (2007). doi:10.1007/978-3-540-70664-9_4
Kluegl, P., Atzmueller, M., Puppe, F.: TextMarker: a tool for rule-based information extraction. In: Chiarcos, C., de Castilho, R.E., Stede, M. (eds.) Proceedings of the Biennial GSCL Conference 2009, 2nd UIMA@GSCL Workshop, pp. 233–240. Gunter Narr Verlag (2009). http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2009-GSCL-TextMarker.pdf
Lafferty, J.D., McCallum, A., Pereira, F.C.N., Fields, C.R.: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001)
Meystre, S., Haug, P.J.: Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J. Biomed. Inform. 39(6), 589–599 (2006)
Nagy, G., Seth, S.: Hierarchical representation of optically scanned documents. In: International Conference on Pattern Recognition, vol. 1, pp. 347–349 (1984)
Nahler, G.: Dictionary of Pharmaceutical Medicine. Springer, Vienna (2009). doi:10.1007/978-3-211-89836-9
Sarawagi, S.: Information extraction. Found. Trends® Databases 1(3), 261–377 (2007)
Thompson, C.A., Califf, M.E., Mooney, R.J.: Active learning for natural language parsing and information extraction. In: ICML, pp. 406–414 (1999)
Wang, X., Chase, H., Markatou, M., Hripcsak, G., Friedman, C.: Selecting information in electronic health records for knowledge acquisition. J. Biomed. Inform. 43(4), 595–601 (2010)
Acknowledgements
This work has been partially funded by the German Federal Ministry of Education and Research (BMBF) (project HUMIT, http://humit.de/, grant no. 01IS14007A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Harmata, S., Hofer-Schmitz, K., Nguyen, PH., Quix, C., Bakiu, B. (2017). Layout-Aware Semi-automatic Information Extraction for Pharmaceutical Documents. In: Da Silveira, M., Pruski, C., Schneider, R. (eds) Data Integration in the Life Sciences. DILS 2017. Lecture Notes in Computer Science(), vol 10649. Springer, Cham. https://doi.org/10.1007/978-3-319-69751-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-69751-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69750-5
Online ISBN: 978-3-319-69751-2
eBook Packages: Computer ScienceComputer Science (R0)