Anomaly Detection Monitoring System for Healthcare

Abstract

Most developing countries suffer from inadequate health care facilities and a lack of medical practitioners as most of them emigrate to developed countries. The outbreak of the COVID-19 pandemic has left these countries more vulnerable to facing the worse outcome of the pandemic. This necessitates the need for a system that continuously monitors patient status and detects how their physiological variables will change over time. As a result, it will reduce the rate of mortality and mitigate the need for medical practitioners to monitor patients continuously. In this work, we show how an autoencoder and extreme gradient boosting can be merged to forecast physiological variables of a patient and detect anomalies and their level of divergence. An accurate detection of current and future anomalies will enable remedial action to be taken by medical practitioners at the right time and possibly save lives.

Anomaly Detection in Time Series: Current Focus and Future Challenges

“…To empower medical professionals to take proactive and timely action, reducing patient transfers and hospital stay lengths, ultimately leading to improved survival rates. But the accuracy of predictions relies heavily on expertly combining machine learning algorithms like autoencoders and extreme gradient boosting (XGBoost) [84].…”

“…They're trained via reconstruction error, only triggering an alert if said error exceeds a predetermined threshold -prompting a swift remedial response. As for XGBoost, this decision tree-based ensemble principle takes physiological variables from time ti as input and outputs variables from the next temporal unit; ti+1 [84].…”