Journal
CLINICAL IMMUNOLOGY
Volume 246, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.clim.2022.109218
Keywords
COVID-19; SARS-CoV2; Laboratory indices; Artificial intelligence; Artificial neural networks
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A prediction model for ICU hospitalization of COVID-19 patients was developed using ANN. Laboratory indices were evaluated to create a database and train ANN models. The alpha-index was used to assess the association of each parameter with outcome. The best ANN achieved high accuracy, precision, sensitivity, and F1-score in predicting ICU hospitalization.
We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (valida-tion), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Di-mers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.
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