Decision Making in Clinical Monitoring: Experts, Expert Systems and Statistics

1988 ◽  
pp. 171-176
Author(s):  
Donald P. Connelly ◽  
Jeffrey B. Rhodes
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Clinical Monitoring

scholarly journals Integrating multiple fuzzy expert systems under restricting requirements 

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 187-196
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Business Processes ◽  
New Product ◽  
Decision Problems ◽  
Food Chains ◽  
Problem Context ◽  
Multiple Factors ◽  
Decision Consistency ◽  
Fuzzy Expert Systems

The multiple, different and specific expertises are often needed in making YES-or-NO (YES/NO) decisions for treating a variety of business, economic, and agricultural decision problems. This is due to the nature of such problems in which decisions are influenced by multiple factors, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertise due to its capability to model real world values which are not always exact, but frequently vague, or uncertain. In addition, they are able to incorporate qualitative factors. The problem of integrating multiple fuzzy expert systems involves several independent and autonomous fuzzy expert systems arranged synergistically to suit a varying problem context. Every expert system participates in judging the problem based on a predefined match between problem context and the required specific expertises. In this research, multiple FESs are integrated through combining their crisp numerical outputs, which reflect the degree of bias to the Yes/No subjective answers. The reasons for independency can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. This article presents simple algorithms to integrate multiple parallel FES under specific requirements: preserving the extreme crisp output values, providing for null or non-participating expertises, and considering decision-related expert systems, which are true requirements of a currently held project. The presented results provides a theoretical framework, which can bring advantage to decision making is many disciplines, as e.g. new product launching decision, food quality tracking, monitoring of suspicious deviation of the business processes from the standard performance, tax and customs declaration issues, control and logistic of food chains/networks, etc. 

Expert systems for decision-making

1990 ◽  
Vol 10 (4) ◽  
pp. 321-322
Author(s):  
A. Brown
Keyword(s):  
Decision Making ◽  
Expert Systems

Fuzzy sets: Decision making, and expert systems

1987 ◽  
Vol 3 (3) ◽  
pp. 221-222
Author(s):  
R.P. van de Riet
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Fuzzy Sets

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

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