Case Representation and Similarity Assessment in the selfBACK Decision Support System
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.
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