Case Representation and Similarity Assessment in the selfBACK Decision Support System
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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.
KeywordsCase-based reasoning Case representations Data streams Similarity assessment
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.
- 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
- 5.Gentner, D., Forbus, K.D.: Mac/fac: a model of similarity-based retrieval. Cogn. Sci. 19, 141–205 (1991)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
- 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)CrossRefGoogle 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 CrossRefGoogle 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