Title :
Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems
Author :
Gholami, Behnood ; Bailey, James M. ; Haddad, Wassim M. ; Tannenbaum, Allen R.
Author_Institution :
Schools of Electr. & Comput. & Biomed. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.
Keywords :
cardiovascular system; closed loop systems; control engineering computing; decision support systems; medical expert systems; multi-agent systems; patient monitoring; probability; ventilation; ICU sedation framework; acute respiratory failure; cardiopulmonary management; cardiovascular function; clinical decision support systems; closed-loop control systems; critical care monitoring; deterministic rule-based expert system; expert systems; intensive care unit; intensive care unit sedation; lifesaving interventions; mechanical ventilation; probability theory; sedative agent administration; Bayesian methods; Biomedical monitoring; Blood pressure; Drugs; Expert systems; Heart rate; Random variables; Bayesian networks; cardiopulmonary management; decision support; expert system; intensive care unit (ICU) sedation; respiratory management; rule-based expert system;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2011.2162412