Advancing Patient Care for Autonomous Saline Level Alert System with Cloud-Based Isolation Forest Algorithm

This work presents a novel method for improving patient care by creating a self-sufficient system to detect saline levels using an Isolation Forest Algorithm (IFA) hosted on the cloud. Standard practices for checking saline levels in IV infusion systems often include human intervention, which might cause important discrepancies to go undetected for longer periods of time. It presents a system that uses machine learning to identify aberrant saline levels in real-time, improving patient safety and relieving healthcare providers of some of their workload. The IFA, a powerful anomaly detection tool that works particularly well with high-dimensional data like that produced by intravenous infusion devices, is at the heart of our strategy. It gains scalability, flexibility, and real-time processing capabilities crucial for early alerting in clinical settings by implementing our algorithm on the cloud. Extensive testing and validation with both synthetic and real-world datasets prove that our suggested approach reliably and effectively detects abnormal saline levels with few false alarms. By providing healthcare practitioners with prompt alerts of possible infusion anomalies and allowing proactive action to avert adverse events, the autonomous saline level alarm system has major implications for patient care. The technology provides smooth interaction with current healthcare infrastructure by moving computing duties to the cloud, which eliminates the need for extra hardware and maintenance burdens.