Case-based decision support system with contextual bandits learning for similarity retrieval model selection

Boomadevi Sekar, Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management KSEM 2018
Place of PublicationSwitzerland
Number of pages7
ISBN (Print)978-3-319-99364-5
Publication statusPublished - 12 Aug 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743

Bibliographical note

11th International Conference , KSEM 2018 : Knowledge Science, Engineering and Management, KSEM ; Conference date: 17-08-2018 Through 19-08-2018


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


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