scholarly journals Decision Support Systems in Animal Health

10.5772/18558 ◽  
2011 ◽  
Author(s):  
Nguyen Van ◽  
Mark Stevenson ◽  
Bryan OLeary
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Animal Health

scholarly journals Decision Support Systems with AHP and SAW Method for Determination of Cattle with Superior Seeds

2016 ◽  
Vol 3 (2) ◽  
pp. 119-128
Author(s):  
Clarissa Amanda Josaputri ◽  
Endang Sugiharti ◽  
Riza Arifudin
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Body Condition Score ◽  
Animal Health ◽  
Animal Husbandry ◽  
Importance Value ◽  
Condition Score ◽  
Hierarchy Process

Department of Animal Husbandry and Fisheries of Semarang District is an institution in charge of livestock and animal health. Basically the Animal Husbandry Department has provided standardization for quality livestock cattle with superior seeds that usually can be judged or measured by various criteria.They are weight, age and value of BCS (Body Condition Score).They needed a system that could help the Department of Livestock and Fisheries of Semarang District in determining the electoral process cattle with superior seeds. In this research, the manufacture of Decision Support Systems in the determination cattle with superior seedsis using a combination of two methods is Analytical Hierarchy Process (AHP) and the Simple Addictive Weighting (SAW). In AHP will perform an importance value calculation criteria that will be paired up with an alternative to the SAW the next process is the sum of the weight from performance rating of all the attributes to each alternative, a ranking conducted to determine the result of cattle with superior seeds. Suggestions on this system, can be developed further by combining other methods to determine the recommendation that more effective.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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