Boloka, Tlou JCrafford, Gerhardus JMokuwe, Mamuku WVan Eden, Beatrice2022-01-102022-01-102021-01Boloka, T.J., Crafford, G.J., Mokuwe, M.W. & Van Eden, B. 2021. Anomaly detection monitoring system for healthcare. http://hdl.handle.net/10204/12209 .978-1-6654-0345-0978-1-6654-4788-1DOI: 10.1109/SAUPEC/RobMech/PRASA52254.2021.9377017http://hdl.handle.net/10204/12209Most 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.FulltextenHealthcare systemsAnomaly detectionAnomaly monitoring systemGradient methodsMedical computingPatient monitoringConference PresentationBoloka, T. J., Crafford, G. J., Mokuwe, M. W., & Van Eden, B. (2021). Anomaly detection monitoring system for healthcare. http://hdl.handle.net/10204/12209Boloka, Tlou J, Gerhardus J Crafford, Mamuku W Mokuwe, and Beatrice Van Eden. "Anomaly detection monitoring system for healthcare." <i>SAUPEC/RobMech/PRASA Conference, Potchefstroom, South Africa, 27-29 January 2021</i> (2021): http://hdl.handle.net/10204/12209Boloka TJ, Crafford GJ, Mokuwe MW, Van Eden B, Anomaly detection monitoring system for healthcare; 2021. http://hdl.handle.net/10204/12209 .TY - Conference Presentation AU - Boloka, Tlou J AU - Crafford, Gerhardus J AU - Mokuwe, Mamuku W AU - Van Eden, Beatrice AB - 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. DA - 2021-01 DB - ResearchSpace DP - CSIR J1 - SAUPEC/RobMech/PRASA Conference, Potchefstroom, South Africa, 27-29 January 2021 KW - Healthcare systems KW - Anomaly detection KW - Anomaly monitoring system KW - Gradient methods KW - Medical computing KW - Patient monitoring LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-0345-0 SM - 978-1-6654-4788-1 T1 - Anomaly detection monitoring system for healthcare TI - Anomaly detection monitoring system for healthcare UR - http://hdl.handle.net/10204/12209 ER -25198