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Peter Gemmar
Professor, Trier University of Applied Sciences, Trier, Germany
Publications
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Research Article
Mortality Prediction for COVID-19 Patients: Methods and Potential
Author(s): Peter Gemmar*
The pandemic spread of Coronavirus leads to increased burden on healthcare services worldwide. Experience shows that required medical treatment can reach limits at local clinics and fast and secure clinical assessment of the disease severity becomes vital. Biomarkers are regularly determined for intensive care patients. Machine learning tools can be used to select appropriate biomarkers in order to estimate the state of health and to predict patient mortality risk. Transparent prediction models allow further statements on the properties and development of the biomarkers in connection with specific health conditions of the intensive care patients. In this work, alternative and advanced model approaches (Support Vector Machine, naive Bayes, Fuzzy system) are compared with models proposed in literature. In addition, aspects such as gender of patients and changes.. View more»