Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets.

ABSTRACT: In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO₂< 60 mmHg, 489 cases) and Non-Severe (PaO₂ ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram

SUBMITTER: Pulgar-Sanchez M 

PROVIDER: S-EPMC8349478 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature