A Mechanism for Decision Support in OOP Applications of Integrated Structural Design

2009 ◽  
Author(s):  
W. Tizani ◽  
A.S. Whitehead
Keyword(s):  
Decision Support ◽  
Structural Design

A structural design of clinical decision support system for chronic diseases risk management

Open Medicine ◽  
2007 ◽  
Vol 2 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chuen-Sheng Cheng
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Structural Design ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision

AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.

A Probabilistic-Based Design Model for Achieving Flexibility in Design

1999 ◽  
Vol 121 (1) ◽  
pp. 77-83 ◽  
Author(s):  
W. Chen ◽  
C. Yuan
Keyword(s):  
Decision Support ◽  
Structural Design ◽  
Decision Model ◽  
Design Model ◽  
Design Performance ◽  
Preference Index ◽  
Flexible Designs ◽  
Design Requirements ◽  
Per Se ◽  
Design Preference

In this paper we propose the use of a probabilistic-based design model as a basis for providing the flexibility in a design process that allows designs to be readily adapted to changing conditions. Our proposed approach can be used to develop a range of solutions that meet a ranged set of design requirements. Meanwhile, designers are allowed to specify the varying degree of desirability of a ranged set of design performance based on their preferences. The Design Preference Index (DPI) is introduced as a design metric to measure the goodness of flexible designs. Providing the foundation to our work are the probabilistic representations of design performance, the application of the robust design concept, and the utilization of the compromise Decision Support Problem (DSP) as a multiobjective decision model. A two-bar structural design is used as an example to demonstrate our approach. Our focus in this paper is on introducing the probabilistic-based design model and not on the results of the example problem, per se.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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