Skip to main content
SpringerLink
Log in

Case Representation and Similarity Assessment in the selfBACK Decision Support System

  • Conference paper
  • First Online:
Book cover Case-Based Reasoning Research and Development (ICCBR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9969))

Included in the following conference series:

Abstract

In this paper we will introduce the selfBACK decision support system that facilitates, improves and reinforces self-management of non-specific low back pain. The selfBACK system is a predictive case-based reasoning system for personalizing recommendations in order to provide relief for patients with non-specific low back pain and increase their physical functionality over time. We present how case-based reasoning is used for capturing experiences from temporal patient data, and evaluate how to carry out a similarity-based retrieval in order to find the best advice for patients. Specifically, we will show how heterogeneous data received at various frequencies can be captured in cases and used for personalized advice.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.med.uio.no/helsam/forskning/grupper/fysioprim/.

  2. 2.

    https://www.ntnu.edu/hunt/hunt3.

References

  1. Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-based reasoning in CARE-PARTNER: gathering evidence for evidence-based medical practice. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 334–345. Springer, Heidelberg (1998). doi:10.1007/BFb0056345

    Chapter  Google Scholar 

  2. Chang, P., Liu, C., Lin, J., Fan, C., Ng, C.S.P.: A neural network with a case based dynamic window for stock trading prediction. Expert Syst. Appl. 36(3), 6889–6898 (2009)

    Article  Google Scholar 

  3. Crow, W.T., Willis, D.R.: Estimating cost of care for patients with acute low back pain: a retrospective review of patient records. J. Am. Osteopath. Assoc. 109(4), 229–233 (2009)

    Google Scholar 

  4. Fritsche, L., Schlaefer, A., Budde, K., Schroeter, K., Neumayer, H.: Recognition of critical situations from time series of laboratory results by case-based reasoning. J. Am. Med. Inform. Assoc. 9(5), 520–528 (2002)

    Article  Google Scholar 

  5. Gentner, D., Forbus, K.D.: Mac/fac: a model of similarity-based retrieval. Cogn. Sci. 19, 141–205 (1991)

    Google Scholar 

  6. Gundersen, O.E.: Toward measuring the similarity of complex event sequences in real-time. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 107–121. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32986-9_10

    Chapter  Google Scholar 

  7. Gundersen, O.E., Sørmo, F., Aamodt, A., Skalle, P.: A real-time decision support system for high cost oil-well drilling operations. AI Mag. 34(1), 21–32 (2013)

    Google Scholar 

  8. Hansen, B.K.: A fuzzy logic-based analog forecasting system for ceiling and visibility. Weather Forecast. 22, 1319–1330 (2007)

    Article  Google Scholar 

  9. Hill, J.C., Whitehurst, D.G.T., Lewis, M., Bryan, S., Dunn, K.M., Foster, N.E., Konstantinou, K., Main, C.J., Mason, E., Somerville, S., Sowden, G., Vohora, K., Hay, E.M.: Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 378(9802), 1560–1571 (2011)

    Article  Google Scholar 

  10. Jære, M.D., Aamodt, A., Skalle, P.: Representing temporal knowledge for case-based prediction. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 174–188. Springer, Heidelberg (2002). doi:10.1007/3-540-46119-1_14

    Chapter  Google Scholar 

  11. Juarez, J.M., Campos, M., Palma, J., Marin, R.: T-care: temporal case retrieval system. Expert Syst. 28(4), 324–338 (2011)

    Article  Google Scholar 

  12. Marling, C., Shubrook, J., Schwartz, F.: Toward case-based reasoning for diabetes management: a prelimenary clinical study and decision support system prototype. Comput. Intell. 25(3), 165–179 (2009)

    Article  MathSciNet  Google Scholar 

  13. Montani, S., Leonardi, G., Bottrighi, A., Portinale, L., Terenziani, P.: Flexible and efficient retrieval of haemodialysis time series. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., Teije, A. (eds.) KR4HC/ProHealth-2012. LNCS (LNAI), vol. 7738, pp. 154–167. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36438-9_11

    Chapter  Google Scholar 

  14. Montani, S.: Case-based decision support in time dependent medical domains. In: Bramer, M. (ed.) IFIP AI 2010. IAICT, vol. 331, pp. 238–242. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15286-3_24

    Chapter  Google Scholar 

  15. Montani, S., Portinale, L.: Case based representation and retrieval with time dependent features. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 353–367. Springer, Heidelberg (2005). doi:10.1007/11536406_28

    Chapter  Google Scholar 

  16. Nilsson, M., Funk, P., Olsson, E.M.G., von Schéele, B., Xiong, N.: Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Artif. Intell. Med. 36(2), 159–176 (2006)

    Article  Google Scholar 

  17. Olsson, E., Funk, P., Xiong, N.: Fault diagnosis in industry using sensor readings and case-based reasoning. J. Intell. Fuzzy Syst. 15(1), 41–46 (2004)

    Google Scholar 

  18. Ram, A.: Continuous case-based reasoning. Artif. Intell. 90(1–2), 25–77 (1997)

    Article  MATH  Google Scholar 

  19. Schmidt, R., Gierl, L.: A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning. Int. J. Med. Inform. 74(2), 307–315 (2005)

    Article  Google Scholar 

  20. Serr, J., Arcos, J.L.: An empirical evaluation of similarity measures for time series classification. Knowl. Based Syst. 67, 305–314 (2014)

    Article  Google Scholar 

  21. van Tulder, M., Becker, A., Bekkering, T., Breen, A., del Real, M.T.G., Hutchinson, A., Koes, B., Laerum, E., Malmivaara, A.: Chapter 3 european guidelines for the management ofacute nonspecific low back painin primary care. Eur. Spine J. 15(2), s169–s191 (2006)

    Article  Google Scholar 

  22. de la Vega, R., Miró, J.: mhealth: a strategic field without a solid scientific soul. A systematic review of pain-related apps. PLoS One 9(7), e:101312 (2014)

    Article  Google Scholar 

  23. Wändell, P., Carlsson, A.C., Wettermark, B., Lord, G., Cars, T., Ljunggren, G.: Most common diseases diagnosed in primary care in stockholm, sweden, in 2011. Fam. Pract. 30(5), 506–513 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

The work has been conducted as part of the selfBACK project, which has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 689043.

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Kerstin Bach, Tomasz Szczepanski, Agnar Aamodt & Odd Erik Gundersen

  2. Department of Public Health and General Practice, Norwegian University of Science and Technology, Trondheim, Norway

    Paul Jarle Mork

Authors
  1. Kerstin Bach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Szczepanski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Agnar Aamodt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Odd Erik Gundersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paul Jarle Mork
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kerstin Bach .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Ashok Goel

  2. Universidad Complutense de Madrid , Madrid, Spain

    M Belén Díaz-Agudo

  3. University of West London , London, United Kingdom

    Thomas Roth-Berghofer

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Bach, K., Szczepanski, T., Aamodt, A., Gundersen, O.E., Mork, P.J. (2016). Case Representation and Similarity Assessment in the selfBACK Decision Support System. In: Goel, A., Díaz-Agudo, M., Roth-Berghofer, T. (eds) Case-Based Reasoning Research and Development. ICCBR 2016. Lecture Notes in Computer Science(), vol 9969. Springer, Cham. https://doi.org/10.1007/978-3-319-47096-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47096-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47095-5

  • Online ISBN: 978-3-319-47096-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation