Paper Info
Title
Case-Based Decision Support System with Contextual Bandits Learning for Similarity Retrieval Model Selection.
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.
Year
Venue
Field
2018
KSEM
Weighting,Feature selection,Multidisciplinary approach,Computer science,Decision support system,Model selection,Artificial intelligence,Clinical decision support system,Case-based reasoning,Software ecosystem,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Booma Devi Sekar143.17
hui wang27617.01