Multi Model Rule Generation for Automatic Drug Administration Systems (original) (raw)
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Soft Methods for Automatic Drug Infusion in Medical Care Environment
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.
Journal of diabetes …, 2008
BackgroundThe objective of this study was to investigate the performance of a newly developed decision support system for the establishment of tight glycemic control in medical intensive care unit (ICU) patients for a period of 72 hours.MethodsThis was a single-center, open, non-controlled feasibility trial including 10 mechanically ventilated ICU patients. The CS-1 decision support system (interacting infusion pumps with integrated enhanced model predictive control algorithm and user interface) was used to adjust the infusion rate of administered insulin to normalize blood glucose. Efficacy and safety were assessed by calculating the percentage of values within the target range (80–110 mg/dl), hyperglycemic index, mean glucose, and hypoglycemic episodes (<40 mg/dl).ResultsThe percentage of values in time in target was 47.0% (±13.0). The average blood glucose concentration and hyperglycemic index were 109 mg/dl (±13) and 10 mg/dl (±9), respectively. No hypoglycemic episode (<40 mg/dl) was detected. Eleven times (1.5% of all given advice) the nurses did not follow and, thus, overruled the advice of the CS-1 system. Several technical malfunctions of the device (repetitive error messages and missing data in the data log) due to communication problems between the new hardware components are shortcomings of the present version of the device. As a consequence of these technical failures of system integration, treatment had to be stopped ahead of schedule in three patients.ConclusionsDespite technical malfunctions, the performance of this prototype CS-1 decision support system was, from a clinical point of view, already effective in maintaining tight glycemic control. Accordingly, and with technical improvement required, the CS-1 system has the capacity to serve as a reliable tool for routine establishment of glycemic control in ICU patients.