Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development
BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
|Authors||Aktar, S.; Ahamad, M. M.; Rashed-Al-Mahfuz, M.; Azad, A.; Uddin, S.; Kamal, A.; Alyami, S. A.; Lin, P. I.; Islam, S. M. S.; Quinn, J. M.; Eapen, V.; Moni, M. A.|
|Publisher Name||JMIR Medical Informatics|
|URL link to publisher's version||https://www.ncbi.nlm.nih.gov/pubmed/33779565|