In Clinical immunology (Orlando, Fla.)
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 (validation), 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-Dimers. 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.
Asteris Panagiotis G, Kokoris Styliani, Gavriilaki Eleni, Tsoukalas Markos Z, Houpas Panagiotis, Paneta Maria, Koutzas Andreas, Argyropoulos Theodoros, Alkayem Nizar Faisal, Armaghani Danial J, Bardhan Abidhan, Cavaleri Liborio, Cao Maosen, Mansouri Iman, Mohammed Ahmed Salih, Samui Pijush, Gerber Gloria, Boumpas Dimitrios T, Tsantes Argyrios, Terpos Evangelos, Dimopoulos Meletios A
2022-Dec-28
Artificial intelligence, Artificial neural networks, COVID-19, Laboratory indices, SARS-CoV2
