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

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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.
Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management KSEM 2018
Pages426-432
ISBN (Electronic)978-3-319-99365-2
DOIs
Publication statusE-pub ahead of print - 12 Aug 2018
Event11th International Conference , KSEM 2018: Knowledge Science, Engineering and Management - China, Changchun, China
Duration: 17 Aug 201819 Aug 2018
https://link.springer.com/chapter/10.1007%2F978-3-319-99365-2_37

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Link
Volume11710
ISSN (Electronic)0302-9743

Conference

Conference11th International Conference , KSEM 2018
Abbreviated titleKSEM
CountryChina
CityChangchun
Period17/08/1819/08/18
Internet address

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Keywords

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

Cite this

Sekar, B., & Wang, H. (2018). Case-based decision support system with contextual bandits learning for similarity retrieval model selection. In Knowledge Science, Engineering and Management KSEM 2018 (pp. 426-432). (Lecture Notes in Computer Science; Vol. 11710 ). https://doi.org/10.1007/978-3-319-99365-2_37