Intelligent Clinical Decision Support System for Managing COPD Patients

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Pulmonology

Background:

• Chronic obstructive pulmonary disease (COPD) is a leading global cause of mortality.
• Health remote monitoring systems (HRMSs) are vital for COPD patient management.
• Analyzing biometric and environmental data can predict COPD patient health deterioration.

Purpose of the Study:

• To review existing research on AI for COPD management.
• To develop an intelligent clinical decision support system (CIDSS) for COPD patients.
• To provide early health evolution information and risk analysis to support COPD treatment.

Main Methods:

• Developed a CIDSS with two modules: vital signs prediction and early warning score calculation.
• Integrated analysis of biometric signs and environmental factors.
• Implemented and assessed the CIDSS in a real-world clinical case.

Main Results:

• The CIDSS effectively generates patient health information and deterioration risks.
• The system provides alerts for out-of-range biometric measurements or significant basal value changes.
• Clinical validation through healthcare professional surveys confirmed the system's utility.

Conclusions:

• The developed CIDSS is a valuable tool for healthcare professionals managing COPD.
• The system facilitates proactive intervention and personalized medical treatment adjustments.
• The CIDSS supports improved patient outcomes in Chronic Obstructive Pulmonary Disease care.