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Missing information prediction in ripple down rule based clinical decision support system

Citation

Hussain, M and Hassan, AU and Sadiq, M and Kang, BH and Lee, S, Missing information prediction in ripple down rule based clinical decision support system, Smart Homes and Health Telematics: Designing a Better Future: Urban Assisted Living 16th International Conference, ICOST 2018 Proceedings, July 10-12, 2018, Singapore, pp. 179-188. ISSN 0302-9743 (2018) [Refereed Conference Paper]

Copyright Statement

Copyright 2018 Springer

DOI: doi:10.1007/978-3-319-94523-1_16

Abstract

Clinical Decision Support System (CDSS) plays an indispensable role in decision making and solving complex problems in the medical domain. However, CDSS expects complete information to deliver an appropriate recommendation. In real scenarios, the user may not be able to provide complete information while interacting with CDSS. Therefore, the CDSS may fail to deliver accurate recommendations. The system needs to predict and complete missing information for generating appropriate recommendations. In this research, we extended Ripple Down Rules (RDR) methodology that identifies the missing information in terms of key facts by analyzing similar previous patient cases. Based on identified similar cases, the system requests the user about the existence of missing facts. According to the userís response, the system resumes current case and infers the most appropriate recommendation. Alternatively, the system generates an initial recommendation based on provided partial information.

Item Details

Item Type:Refereed Conference Paper
Keywords:information prediction, ripple down rules, clinical decision support system
Research Division:Health Sciences
Research Group:Health services and systems
Research Field:Health informatics and information systems
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Information services not elsewhere classified
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:138291
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2020-03-31
Last Modified:2020-05-04
Downloads:0

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