Automated Post-Surgical Monitoring: Machine Learning for Early Complication Detection

Abstract

The integration of machine learning (ML) in automated post-surgical monitoring represents a pivotal advancement in healthcare, aiming to enhance patient outcomes by enabling early detection of complications. This research critically examines the efficacy of various ML algorithms in predicting post-operative complications through real-time analysis of preoperative and intraoperative data. Utilizing multimodal approaches, including wearable sensors and biometric data, the study highlights the potential for improving patient surveillance and timely clinical responses, thereby mitigating risks associated with complications such as acute kidney injury, pneumonia, and delirium. The findings reveal that ML models not only provide accurate predictions but also translate complex data into clinically meaningful interpretations, facilitating informed decision-making within the perioperative care continuum. Moreover, the study emphasizes the significance of continuous monitoring as a method to foster timely interventions, thereby decreasing hospital morbidity and mortality. By leveraging the capabilities of ML, this research underscores the transformative potential of automated monitoring systems in surgical settings, advocating for their broader implementation to enhance patient safety and optimize postoperative care management.