Isolation Forests Integration for Proactive Anomaly Detection in Augmented Reality-enhanced Tele-ICU Systems

To improve patient monitoring and identify aberrant occurrences quickly, use isolation forests in augmented reality (AR)-enhanced tele-intensive care unit (Tele-ICU) systems for proactive anomaly detection. Here, we present a system that uses the ensemble machine learning technique of isolation forests to spot anomalies in the complicated and ever-changing data streams in tele-ICU settings. Our solution finds abnormalities in AR-enhanced data better than standard approaches because it uses isolation trees, which separate anomalies more rapidly. This improves healthcare quality and patient safety. The system can adjust to changing patient circumstances by incorporating isolation forests, which solve problems caused by high-dimensional and various AR-enhanced tele-ICU data. It can quickly discover anomalies and respond proactively to health problems by building isolation trees using randomly picked information. Integrating isolation forests is in sync with the real-time requirements of tele-ICU systems, enabling the rapid detection and prevention of abnormalities before worsening. By offering a practical and efficient method for detecting anomalies, the proposed framework shows potential for improving the overall performance of tele-ICU systems. To ensure the smooth functioning and safety of AR-enhanced tele-ICU settings, which contribute to better patient outcomes and healthcare delivery, healthcare organizations are increasingly embracing technological innovations. One essential tool in this integration is isolation forests.