Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.
With no effective treatment currently available and maximum preventive measures already in place, more interventions in the clinical field are needed to decrease COVID-19 patient mortality. Early prediction of mortality risk in COVID-19 patients can decrease mortality by assuring efficient resource allocation and treatment planning. This study conducts an early prediction of COVID-19 prognosis using laboratory, clinical, and demographic data collected from patients in the first day of admission. Three machine learning models were developed to investigate and compare the prediction power of invasive and noninvasive biomarkers. The results suggest that early mortality prediction of patients via non-invasive biomarkers provides significant accuracy and can be used as a triage assisting tool without the need for
additional costs or waiting time of laboratory tests.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.