Intelligent decision support systems for mechanical ventilation

SummaryObjective rview of different methodologies used in various intelligent decision support systems (IDSSs) for mechanical ventilation is provided. The applications of the techniques are compared in view of todayʹs intensive care unit (ICU) requirements. s ation available in the literature is utilized to provide a methodological review of different systems. s isons are made of different systems developed for specific ventilation modes as well as those intended for use in wider applications. The inputs and the optimized parameters of different systems are discussed and rule-based systems are compared to model-based techniques. The knowledge-based systems used for closed-loop control of weaning from mechanical ventilation are also described. Finally, in view of increasing trend towards automation of mechanical ventilation, the potential utility of intelligent advisory systems for this purpose is discussed. sions for mechanical ventilation can be quite helpful to clinicians in todayʹs ICU settings. To be useful, such systems should be designed to be effective, safe, and easy to use at patientʹs bedside. In particular, these systems must be capable of noise removal, artifact detection and effective validation of data. Systems that can also be adapted for closed-loop control/weaning of patients at the discretion of the clinician, may have a higher potential for use in the future.