Paper Info
Title
MedCase: A template medical case store for case-based reasoning in medical decision support
Abstract
The early development of medical decision support systems (appeared as expert systems (ES)) mainly focused on, among others, rule-based reasoning (RBR) and decision table/tree (DT) methods as problem solving strategies. These efforts were novel at the time; however, as these methodologies applied to more complex situations, the construction of knowledge base (e.g. rules, cases and 'models') for specific problem solving tasks becomes difficult and time consuming. Remedies to these difficulties have been sought, aiming at better knowledge modeling, knowledge acquisition, and extending the problem solving paradigm to distributed architectures. Alternatively, case-based reasoning (CBR) provides a different problem solving paradigm. In CBR, the knowledge is seen as cases that contain explicit and implicit aspects of the knowledge for solving a problem. The CBR methodology works in a practical way, and the reasoning is based on recalled knowledge memory of solved cases. To alleviate the difficulty of knowledge (case) acquisition and construction, this paper presents a design of a template case store, called MedCase. MedCase utilizes the semantic web technologies and supports a distributed CBR system architecture. MedCase promotes an open and accessible architecture for common CBR tasks in a virtual Healthcare Enterprise environment. MedCase will also allow the construction and sharing of cases that facilitate the development of distributed CBR-based medical decision support systems.
Year
DOI
Venue
2013
10.1145/2492517.2500331
ASONAM
Keywords
Field
DocType
specific problem,cbr system architecture,knowledge base,case-based reasoning,virtual healthcare enterprise environment,inference mechanisms,decision tree methods,knowledge memory,rule-based reasoning,better knowledge modeling,decision table methods,cbr methodology work,medical decision support system,problem solving strategies,medcase,common cbr task,decision support systems,template medical case store,cbr,knowledge acquisition,different problem,rbr,medical computing,knowledge modeling,medical decision support systems,decision trees,multidimensional scaling,visualization,case based reasoning,clustering
Data mining,Knowledge representation and reasoning,Intelligent decision support system,Computer science,Model-based reasoning,Artificial intelligence,Knowledge base,Case-based reasoning,Reasoning system,Knowledge acquisition,Machine learning,Knowledge modeling
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Hsien-Tseng Wang100.68
Abdullah Uz Tansel2576224.29