Author: Darapaneni, N.; Gupta, M.; Paduri, A. R.; Agrawal, R.; Padasali, S.; Kumari, A.; Purushothaman, P.
Title: A Novel Machine Learning Based Screening Method For High-Risk Covid-19 Patients Based On Simple Blood Exams Cord-id: 492cd9wl Document date: 2021_1_1
ID: 492cd9wl
Snippet: This paper presents a predictive model to potentially identify high-risk COVID-19 infected patients based on easily analyzed circulatory blood markers. These findings can enable effective and efficient care programs for high-risk patients and periodic monitoring for the low-risk ones, thereby easing the hospital flow of patients and can further be utilized for hospital bed utilization assessment. The present machine learning-based SV-LAR model results in a high 87% f1 score, harmonic mean of 91%
Document: This paper presents a predictive model to potentially identify high-risk COVID-19 infected patients based on easily analyzed circulatory blood markers. These findings can enable effective and efficient care programs for high-risk patients and periodic monitoring for the low-risk ones, thereby easing the hospital flow of patients and can further be utilized for hospital bed utilization assessment. The present machine learning-based SV-LAR model results in a high 87% f1 score, harmonic mean of 91% precision, and 83% recall to classify COVID-19, infected patients, as high-risk patients needing hospitalization.
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