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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
ICD-GM: “International Classification of Diseases, German Modification” is the official classification for diagnoses in outpatient and inpatient health care in Germany.
References
Köppen, V., Saake, G., Sattler, K.-U.: Data Warehouse Technologien. MITP (2014). ISBN 9783826694851
Tolxdorff, T., Puppe, F.: Klinisches Data Warehouse. Informatik-Spektrum 39, 233–237 (2016). https://doi.org/10.1007/s00287-016-0968-3
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-0038-1651497
Dietrich, G., et al.: Ad hoc information extraction for clinical data warehouses. Methods Inf. Med. 57, e22–e29 (2018). https://doi.org/10.3414/ME17-02-0010
Kharat, A., Singh, A., Kulkarni, V., Shah, D.: Data mining in radiology. Indian J. Radiol. Imaging 24, 97 (2014). https://doi.org/10.4103/0971-3026.134367
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/s10278-012-9531-1
Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing, Birmingham (2010). ISBN 978-1-849513-60-9
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
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.2015.04.007
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.2015.11.032
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Fiebeck, J., Laser, H., Winther, H.B., Gerbel, S. (2019). Leaving No Stone Unturned: Using Machine Learning Based Approaches for Information Extraction from Full Texts of a Research Data Warehouse. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-06016-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06015-2
Online ISBN: 978-3-030-06016-9
eBook Packages: Computer ScienceComputer Science (R0)