Automated, real-time hospital ICU emergency signaling: A field-level implementation

TL;DR

This paper presents a cost-effective, real-time ICU emergency signaling system using machine learning, suitable for resource-limited hospitals, with a prototype demonstrating reliable critical event detection.

Contribution

It introduces a novel, inexpensive ICU monitoring system with a simple interface and a Kernel-based On-line Anomaly Detection algorithm for emergency signaling.

Findings

Reliable real-time detection of critical events

Low false alarm rate demonstrated in prototype

Suitable for resource-limited hospital settings

Abstract

Contemporary patient surveillance systems have streamlined central surveillance into the electronic health record interface. They are able to process the sheer volume of patient data by adopting machine learning approaches. However, these systems are not suitable for implementation in many hospitals, mostly in developing countries, with limited human, financial, and technological resources. Through conducting thorough research on intensive care facilities, we designed a novel central patient monitoring system and in this paper, we describe the working prototype of our system. The proposed prototype comprises of inexpensive peripherals and simplistic user interface. Our central patient monitoring system implements Kernel-based On-line Anomaly Detection (KOAD) algorithm for emergency event signaling. By evaluating continuous patient data, we show that the system is able to detect critical events in real-time reliably and has low false alarm rate.