A novel framework for COPD management in cyber-physical systems using machine learning.

Area of Science:

• Computer Science and Engineering
• Medical Informatics
• Artificial Intelligence in Healthcare

Background:

• Chronic Obstructive Pulmonary Disease (COPD) exacerbations present significant healthcare challenges due to their unpredictability and severe patient impact.
• Existing COPD prediction models often lack real-time capabilities and fail to integrate multi-source data for accurate forecasting.

Purpose of the Study:

• To develop and validate a Cyber-Physical System (CPS)-enabled framework for real-time prediction of COPD exacerbations.
• To integrate diverse data sources, including clinical and online data, for enhanced prediction accuracy.
• To leverage advanced machine learning techniques for robust COPD exacerbation forecasting.

Main Methods:

• Implementation of a Cyber-Physical System (CPS) framework integrating primary (clinical) and secondary (online) data.
• Application of machine learning algorithms, including Random Forest and Artificial Neural Networks, for feature selection and prediction.
• Statistical validation using ANOVA for harmonizing diverse data sources and ensuring model robustness.

Main Results:

• The proposed framework demonstrated effectiveness in real-time COPD exacerbation prediction.
• Key performance metrics including accuracy, precision, recall, F1-score, and AUC indicated strong predictive potential.
• The system facilitates early detection of COPD exacerbations.

Conclusions:

• The CPS-enabled framework offers a proactive approach to managing COPD exacerbations.
• The system supports timely alerts, accurate forecasting, and informed clinical decision-making.
• This approach has the potential to improve patient outcomes and reduce healthcare expenditures.