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Leaving No Stone Unturned: Using Machine Learning Based Approaches for Information Extraction from Full Texts of a Research Data Warehouse

  • Johanna FiebeckEmail author
  • Hans Laser
  • Hinrich B. Winther
  • Svetlana GerbelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)

Abstract

Data in healthcare and routine medical treatment is growing fast. Therefore and because of its variety, possible correlation within these are becoming even more complex. Popular tools for facilitating the daily routine for the clinical researchers are more often based on machine learning (ML) algorithms. Those tools might facilitate data management, data integration or even content classification. Besides commercial functionalities, there are many solutions which are developed by the user himself for his own, specific question of research or task. One of these tasks is described within this work: qualifying the Weber fracture, an ankle joint fracture, from radiological findings with the help of supervised machine learning algorithms. To do so, the findings were firstly processed with common natural language processing (NLP) methods. For the classifying part, we used the bags-of-words-approach to bring together the medical findings on the one hand, and the metadata of the findings on the other hand, and compared several common classifier to have the best results. In order to conduct this study, we used the data and the technology of the Enterprise Clinical Research Data Warehouse (ECRDW) from Hannover Medical School. This paper shows the implementation of machine learning and NLP techniques into the data warehouse integration process in order to provide consolidated, processed and qualified data to be queried for teaching and research purposes.

Keywords

Clinical Research Data Warehouse Machine learning Text mining Data science Unstructured data Secondary use Radiology NLP 

References

  1. 1.
    Köppen, V., Saake, G., Sattler, K.-U.: Data Warehouse Technologien. MITP (2014). ISBN 9783826694851Google Scholar
  2. 2.
    Tolxdorff, T., Puppe, F.: Klinisches Data Warehouse. Informatik-Spektrum 39, 233–237 (2016).  https://doi.org/10.1007/s00287-016-0968-3CrossRefGoogle Scholar
  3. 3.
    Zapletal, E., Bibault, J.-E., Giraud, P., Burgun, A.: Integrating multimodal radiation therapy data into i2b2. Appl. Clin. Inform. 09, 377–390 (2018).  https://doi.org/10.1055/s-0038-1651497CrossRefGoogle Scholar
  4. 4.
    Dietrich, G., et al.: Ad hoc information extraction for clinical data warehouses. Methods Inf. Med. 57, e22–e29 (2018).  https://doi.org/10.3414/ME17-02-0010CrossRefGoogle Scholar
  5. 5.
    Kharat, A., Singh, A., Kulkarni, V., Shah, D.: Data mining in radiology. Indian J. Radiol. Imaging 24, 97 (2014).  https://doi.org/10.4103/0971-3026.134367CrossRefGoogle Scholar
  6. 6.
    Do, B.H., Wu, A.S., Maley, J., Biswal, S.: Automatic retrieval of bone fracture knowledge using natural language processing. J. Digit. Imaging 26, 709–713 (2013).  https://doi.org/10.1007/s10278-012-9531-1CrossRefGoogle Scholar
  7. 7.
    Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing, Birmingham (2010). ISBN 978-1-849513-60-9Google Scholar
  8. 8.
    Daumke, P., Simon, K., Paetzold, J., Marwede, D., Kotter, E.: Data-Mining in radiologischen Befundtexten. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der Bildgeb. Verfahren 182, WS117_3 (2010).  https://doi.org/10.1055/s-0030-1252462
  9. 9.
    Kavuluru, R., Rios, A., Lu, Y.: An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artif. Intell. Med. 65, 155–166 (2015).  https://doi.org/10.1016/j.artmed.2015.04.007CrossRefGoogle Scholar
  10. 10.
    McNutt, T.R., Moore, K.L., Quon, H.: Needs and challenges for big data in radiation oncology. Int. J. Radiat. Oncol. Biol. Phys. 95, 909–915 (2016).  https://doi.org/10.1016/j.ijrobp.2015.11.032CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Information ManagementHannover Medical SchoolHannoverGermany
  2. 2.Institute for Diagnostic and Interventional RadiologyHannover Medical SchoolHannoverGermany

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