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

• Authors: Chi-Chang Chang , Chuen-Sheng Cheng
• Languages of publication: EN

Abstracts

EN

In 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.

Keywords

EN

Clinical Decision Support System (CDSS); Bayesian Decision Theory; nonhomogeneous Poisson process; (NHPP) Chronic Diseases Risk Management

• Publisher: De Gruyter Open
• Journal: Open Medicine
• Year: 2007
• Volume: 2
• Issue: 2
• Pages: 129-139

Dates

published

1 - 6 - 2007

online

1 - 6 - 2007

Contributors

author

Chi-Chang Chang

• Department of Information Management Science, Chung Shan Medical University, Taichung, Taiwan, R.O.C

author

Chuen-Sheng Cheng

• Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, 334, Taiwan, R.O.C

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Identifiers

DOI

10.2478/s11536-007-0021-7