Enhancing context-aware SARS disorder management: a proposed multi-agent simulation framework with machine learning...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Computational Biology

Background:

• Severe Acute Respiratory Distress (SARS) disorder presents with critical symptoms like abnormal respiratory rate and oxygen saturation.
• Managing SARS disorder requires integrating diverse data sources, including biosensor readings and contextual information.
• Existing systems may struggle with uncertainty and inconsistencies in biosensor data.

Purpose of the Study:

• To develop and analyze a context-aware management system for SARS disorder.
• To implement a multi-agent simulation framework for real-time analysis.
• To reduce uncertainty and handle inconsistencies in biosensor data through context-sensitive reasoning.

Main Methods:

• Utilized NetLogo for multi-agent simulation framework development.
• Integrated a knowledge-based inference component combining physiological and contextual data.
• Employed machine learning classifiers: Naïve Bayes, Multinomial Naïve Bayes, Decision Table, Logistic Regression, and SMO.
• Evaluated system performance using TP, FP, Precision, Recall, F-Measure, MCC, ROC, and PRC.

Main Results:

• The system effectively integrates physiological, environmental, and patient history data.
• Machine learning classifiers demonstrated varying degrees of suitability for the SARS disorder context.
• Probabilistic forecasts were generated to aid in alerting patients and informing clinical staff.

Conclusions:

• The developed framework facilitates context-specific healthcare responses for SARS disorder.
• Multi-agent simulation and context-aware reasoning enhance decision-making in respiratory distress management.
• The study highlights the potential of AI and simulation in improving patient monitoring and care.