A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units

Abstract

Generalized linear mixed models (GLMM) combined with the L1 penalty (Least Absolute Shrinkage and Selection Operator/LASSO) is called LASSO GLMM. LASSO GLMM reduces overfitting and selects predictor variables in modeling. The aim of this study is to evaluate the model's performance for predicting Covid-19 patients with certain congenital disease that require ICU based on the results of blood tests laboratory and patient’s vital signs. This study used binary response variables, 1 if the patient was admitted to the ICU and 0 if the patient was not admitted to the ICU. The fixed effect predictor variables are the results of blood tests laboratory and patient’s vital signs. The random effect predictor variable is patient's congenital disease. The result showed that the average of accuracy and AUC from LASSO GLMM is more than the average of accuracy and AUC from LASSO GLM by using 5% level of significance. Respiratory rate and Lactate show a significance effect to predict the ICU needs of Covid-19 patients. The random effects patient's congenital disease has significance effect at 5% level of significance. It means that the ICU needs for Covid-19 patients varies among patient's congenital disease. We can conclude that GLMM LASSO with the random effect of patient’s congenital diseases has better modeling performance to predict the ICU needs of Covid-19 patients based on the results of blood tests laboratory and patient’s vital signs. The results of this modeling can quickly detect Covid-19 patients who need the ICU and can help medical staff use ICU resources optimally