Processed_Model_Data.zip

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.