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Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection

  • Booma Devi Sekar
  • Hui Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Case-based reasoning has become one of the well-sought approaches that supports the development of personalized medicine. It trains on previous experience in form of resolved cases to provide solution to a new problem. In developing a case-based decision support system using case-based reasoning methodology, it is critical to have a good similarity retrieval model to retrieve the most similar cases to the query case. Various factors, including feature selection and weighting, similarity functions, case representation and knowledge model need to be considered in developing a similarity retrieval model. It is difficult to build a single most reliable similarity retrieval model, as this may differ according to the context of the user, demographic and query case. To address such challenge, the present work presents a case-based decision support system with multi-similarity retrieval models and propose contextual bandits learning algorithm to dynamically choose the most appropriate similarity retrieval model based on the context of the user, query patient and demographic data. The proposed framework is designed for DESIREE project, whose goal is to develop a web-based software ecosystem for the multidisciplinary management of primary breast cancer.

Keywords

Case-based reasoning Clinical decision support system Similarity retrieval Contextual bandits learning 

Notes

Acknowledgments

The DESIREE project has received funding from the European Union´s Horizon 2020 research and innovation program under grant agreement No. 690238.

References

  1. 1.
    Parra-Calderón, C.L.: Patient similarity in prediction models based on health data: A scoping review, JMIR Med Inform. 5(1) (2017)Google Scholar
  2. 2.
    Alexandrini, F., Krechel, D., Maximini, K., Wanggenheim, A.: Integrating CBR into the health caser organization. In: 16th IEEE Symposium on Computer-Based Medical Systems (2003)Google Scholar
  3. 3.
    Mary, J., Gaudel, R., Preux, P.: Bandits and recommender systems. In: Pardalos, P., Pavone, M., Farinella, G.M., Cutello, V. (eds.) MOD 2015. LNCS, vol. 9432, pp. 325–336. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27926-8_29CrossRefGoogle Scholar
  4. 4.
    Séroussi, B, et al.: Reconciliation of multiple guidelines for decision support: a case study on the multidisciplinary management of breast cancer within the DESIREE project. In: 2017 Proceedings of the AMIA Annual Symposium, Washington DC, 4–8 November 2017 (2017)Google Scholar
  5. 5.
    Larburu, N., et al.: Augmenting guideline-based CDSS with experts’ knowledge. In: 10th International Conference on Health Informatics, Porto, Portugal, 21–23 February 2017 (2017)Google Scholar
  6. 6.
    Bouneffouf, D., Feraud, R.: Multi-armed bandit problem with known trend. Neurocomputing 205, 16–21 (2016)CrossRefGoogle Scholar
  7. 7.
    Langford, J., Zhang, T.: The Epoch-Greedy algorithm for multi-armed bandits with side information. In: Advances in Neural Information Processing System, pp. 817–824 (2008)Google Scholar
  8. 8.
    Bouneffouf, D., Bouzeghoub, A., Gançarski, A.L.: A contextual-bandit algorithm for mobile context-aware recommender system. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7665, pp. 324–331. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34487-9_40CrossRefGoogle Scholar
  9. 9.
    Agrawal, S., Goyal, N.: Thompson sampling for contextual bandits with linear payoffs. In: ICML (3), pp. 127–135 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of ComputingUlster UniversityNewtownabbeyNorthern Ireland, UK

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