Skip to main content

Patient Records Retrieval System for Integrated Care in Treatment of Cervical Spine Defect

  • Conference paper
  • First Online:
Data Management and Analytics for Medicine and Healthcare (DMAH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10186))

Abstract

In clinical decision making, information on the treatment of patients that show similar medical conditions and symptoms to the current case, is one of most relevant information sources to create a good, evidence-based treatment plan. However, the retrieval of similar cases is still challenging and automatic support is missing. The reasons are two-fold: First, the query formulation is difficult since multiple criteria need to be selected and specified in short query phrases. Second, the discrete storage of multimedia patient records makes the retrieval and summary of a patient history extremely difficult. In this paper, we present a retrieval system for electronic health records (EHR). More specifically, a retrieval platform for EHRs for supporting clinical decision making in treatment of cervical spine defects with the information extracted from textual data of patient records is implemented as prototype. The patient cases are classified according to cervical spine defect classes, while the classification relies upon rules obtained from the corresponding defect classification schema and guidelines. In a retrospective study, the classifier is applied to clinical documents and the classification results are evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://lucene.apache.org/solr/.

References

  1. Muhle, C., Metzner, J., Weinert, D., Falliner, A., Brinkmann, G., Mehdorn, M.H., Heller, M., Resnick, D.: Classification system based on kinematic MR imaging in cervical spondylitic myelopathy. Am. J. Neuroradiol. 19(9), 1763–1771 (1998)

    Google Scholar 

  2. von Sachsen S.: Computer aided defect classification for model-based therapy of cervical spinal stenosis. In: International Conference on Biomedical Engineering and Systems (ICBES), Prague, Czech Republic (2014)

    Google Scholar 

  3. Daenzer, S., Freitag, S., von Sachsen, S., Steinke, H., Groll, M., Meixensberger, J., Leimert, M.: Volhog: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI. Med. Phys. 41(8), 082305 (2014)

    Article  Google Scholar 

  4. Schizas, C., Theumann, N., Burn, A., Tansey, R., Wardlaw, D., Smith, FW., Kulik, G.: Qualitative grading of severity of lumbar spinal stenosis based on the morphology of the dural sac on magnetic resonance images. Spine (Phila Pa 1976) 35(21, 9), 1919–1924 (2010)

    Google Scholar 

  5. Samwald, M., Fehre, K., de Bruin, J., Adlassnig, K.-P.: The arden syntax standard for clinical decision support. J. Biomed. Inform. 45(4), 711–718 (2012)

    Article  Google Scholar 

  6. Huang, Z., Teije, A., Harmelen, F.: Rule-based formalization of eligibility criteria for clinical trials. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS (LNAI), vol. 7885, pp. 38–47. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38326-7_7

    Chapter  Google Scholar 

  7. Kelly, D., Sugimoto, C.R.: A systematic review of interactive information retrieval evaluation studies, 1967–2006. J. Am. Soc. Inform. Sci. Technol. 64(4), 745–770 (2013)

    Article  Google Scholar 

  8. Zhou, Y., Amundson, P.K., Fang, Y., Kessler, M.M., Benzinger, T.L.S., Wippold, F.J.: Automated classification of radiology reports to facilitate retrospective study in radiology. J. Digital Imaging 27(6), 730–736 (2014)

    Article  Google Scholar 

  9. Claster, W., Shanmuganathan, S., Ghotbi, N.: Text mining in radiological data records: an unsupervised neural network approach. In: First Asia International Conference on Modelling Simulation, AMS 2007, pp. 329–333, March 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kerstin Denecke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Deng, Y., Denecke, K. (2017). Patient Records Retrieval System for Integrated Care in Treatment of Cervical Spine Defect. In: Wang, F., Yao, L., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2016. Lecture Notes in Computer Science(), vol 10186. Springer, Cham. https://doi.org/10.1007/978-3-319-57741-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57741-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57740-1

  • Online ISBN: 978-3-319-57741-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy