Decision Analysis and Decision Support Systems in Anatomic Pathology

2011 ◽  
pp. 173-186
Author(s):  
Michael Hendrickson ◽  
Bonnie Balzer
Keyword(s):  
Decision Support ◽  
Decision Analysis ◽  
Decision Support Systems ◽  
Support Systems ◽  
Anatomic Pathology

scholarly journals DECISION CONCEPTS

2014 ◽  
pp. 18-22
Author(s):  
Miki Sirola
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Analysis ◽  
Decision Support Systems ◽  
Production Planning ◽  
Support Systems ◽  
Detailed Examination ◽  
Decision Models ◽  
Decision Science ◽  
Knowledge Based

Decision making is done in many application areas. Still most studies are done in such fields as economy and production planning. In methodologies used there exists more variation. This paper reviews the decision concepts discussed in the literature. Also some decision models by the author are commented. The field and practise in decision science is summarized. Although decision support systems are the final results of many projects, they are mostly based on the decision concepts behind the studies that deserve also more detailed examination. Decision analysis approach and knowledge-based technologies are examples of commonly used concepts.

Value and Values

2021 ◽  
pp. 13-31
Author(s):  
Charles E. Phelps ◽  
Guru Madhavan
Keyword(s):  
Decision Support ◽  
Decision Analysis ◽  
Decision Support Systems ◽  
Systems Engineering ◽  
Support Systems ◽  
Objective Data ◽  
Group Choice ◽  
Wine Quality ◽  
Good Decision ◽  
Individual Preferences

Many decisions are done intuitively. Sometimes, this works well, and sometimes they lead us astray. Tools of systems engineering recognize human biases and ask about what we do best—specify what is most important to us under the circumstances. This chapter presents a brief introduction into this world (multi-criteria decision analysis) using an example comparing three fictitious wines to show how different preferences lead to different rankings of wine quality, even when using the same “objective” data. This is as it should be—tastes differ, and good decision support systems take this into account. At this point, we focus on decisions made by one person. Later chapters focus on combining a diversity of individual preferences into a group choice.

Intelligent DSS Under Verbal Decision Analysis

2011 ◽  
pp. 514-527 ◽  
Author(s):  
Ilya Ashikhmin ◽  
Eugenia Furems ◽  
Alexey Petrovsky ◽  
Michael Sternin
Keyword(s):  
Decision Support ◽  
Decision Analysis ◽  
Decision Support Systems ◽  
Support Systems ◽  
Expert Knowledge ◽  
Methodological Approach ◽  
Problem Solution ◽  
Problem Description ◽  
Verbal Decision Analysis ◽  
Main Ideas

Verbal decision analysis (VDA) is a relatively new term introduced in Larichev and Moshkovich (1997) for a methodological approach to discrete multi-criteria decision making (MCDM) problems that was under elaboration by Russian researchers since the 1970s. Its main ideas, principles, and strength in comparison with other approaches to MCDM problems are summarized in Moshkovich, Mechitov, and Olson (2005) and in posthumous book (Larichev, 2006) as follows: problem description (alternatives, criteria, and alternatives’ estimates upon criteria) with natural language without any conversion to numerical form; usage of only those operations of eliciting information from a decision maker (DM) that deems to be psychologically reliable; control of DM’s judgments consistency, and traceability of results, that is, the intermediate and final results of a problem solution have to be explainable to DM. The main objective of this chapter is to provide an analysis of the methods and models of VDA for implementing them in intellectual decision support systems. We start with an overview of existing approaches to VDA methods and model representation. In the next three sections we present examples of implementing the methods and models of VDA for intellectual decision support systems designed for such problems solving as discrete multi-criteria choice, construction of expert knowledge base, and multi-criteria assignment problem. Finally, we analyze some perspective of VDA-based methods to implement them for intellectual decision support systems.

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.

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