Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

“…Our results are in line with partial results obtained by previous studies, as the variables identified in our research correspond to subgroups reported across different studies in the literature. Specifically, our findings on LDH, age, red blood cell distribution standard deviation, neutrophils, basophils, and C-reactive protein for severe and non-severe COVID-19 cases are supported by the results obtained across various prior investigations [48] , [49] , [87] , [88] , [89] . However, the difference in weight across the variables could be due to variations among different populations or settings [90] .…”

LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19

“…In addition, Yang et al. [17] and Huyut and Huyut [18] . reported that diagnoses based on RBV values could be a rapid and more cost-effective alternative tool with high accuracy for the diagnosis and prediction of the prognosis of COVID-19.…”

Cytokine expression and mortality risk among COVID-19 hospitalized patients over 60 years of age in a referral hospital in Cartagena, Colombia

“…Overall, our findings are consistent with those previously reported [ [27] , [28] , [29] , [30] ]. Regarding other laboratory parameters, consistent associations have been documented between increased levels of C-reactive protein [ 31 , 32 ], ferritin [ 32 , 33 ], INR [ 33 ], and NLR [ 34 ] in patients with negative COVID-19 outcomes, such as severity or death. However, these associations were not observed in our study.…”