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COVID-19 Machine Learning Based Survival Analysis and Discharge Time Likelihood Prediction Using Clinical Data
7 Pages Posted: 28 Apr 2020 Publication Status: Published
More...Abstract
As a highly contagious respiratory disease, Coronavirus 2019 (COVID-19) has yielded high mortality rates around the globe since its emergence in December of 2019. As the number of COVID-19 cases soars in epicenters, health officials are warning about the possibility of the designated treatment centers being overwhelmed by severely ill coronavirus patients. In this study, several computational techniques are implemented to analyze the survival characteristics of more than 1182 patients. In particular, two features of gender and age are used as modeling parameters. The computational results agree with the data reported in early clinical reports released for a sample group of patients from China that confirmed a higher mortality rate in men compared to women. The discharge time of COVID-19 patients was also evaluated using different machine learning and statistical analysis methods. The results indicate that the Gradient Boosting survival analysis model outperforms other techniques. This research study is aimed to help health officials make more educated decisions during the outbreak.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Conflict of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Keywords: COVID-19, Survival Analysis, Machine Learning, Statistical Analysis
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