Intelligent decision support systems for mechanical ventilation

Area of Science:

• Biomedical Engineering
• Critical Care Medicine
• Artificial Intelligence in Healthcare

Background:

• Mechanical ventilation is a cornerstone of intensive care unit (ICU) management for critically ill patients.
• Intelligent Decision Support Systems (IDSSs) are increasingly explored to aid clinicians in complex ventilation decisions.
• Current ICU requirements necessitate advanced tools for optimizing patient ventilation and weaning processes.

Purpose of the Study:

• To provide a comprehensive overview of diverse methodologies employed in IDSSs for mechanical ventilation.
• To compare the applications of various IDSS techniques against current ICU demands.
• To discuss the potential utility of intelligent advisory systems in the context of increasing mechanical ventilation automation.

Main Methods:

• A literature-based methodological review of existing IDSSs for mechanical ventilation.
• Comparison of systems designed for specific ventilation modes versus broader applications.
• Analysis of system inputs, optimized parameters, and comparison of rule-based versus model-based techniques.

Main Results:

• Various IDSSs have been developed for specific ventilation modes and wider applications.
• Rule-based systems and model-based techniques show different strengths and weaknesses.
• Knowledge-based systems for closed-loop weaning are described, highlighting their role in patient management.
• Intelligent advisory systems show potential for automating mechanical ventilation.

Conclusions:

• IDSSs can significantly assist clinicians in ICU settings, improving patient care.
• Essential design features for effective IDSSs include safety, ease of use, noise removal, artifact detection, and data validation.
• Systems adaptable for clinician-directed closed-loop control and weaning hold promise for future clinical integration.