SARS-CoV-2 severity prediction in young adults using artificial intelligence
نویسندگان
چکیده
Aim: to build a predictive model for severe COVID-19 prediction in young adults using deep learning methods. Materials and methods: data from 906 medical records of patients aged 18 44 years with laboratory-confirmed SARS-CoV-2 infection during 2020–2021 period was analyzed. Evaluation laboratory instrumental carried out the Mann-Whitney U-test. The level statistical significance p≤0,05. neural network trained Pytorch framework. Results: mild moderate infection, peripheral oxygen saturation, erythrocytes, hemoglobin, total protein, albumin, hematocrit, serum iron, transferrin, absolute blood eosinophil lymphocyte counts were significantly higher than СOVID-19 (p< 0,001). values number neutrophils, ESR, glucose, ALT, AST, CPK, urea, LDH, ferritin, CRP, fibrinogen, D-dimer, respiration rate, heart pressure group severity statistically lower (p < 0.001). Eleven indicators identified as predictors (peripheral level, erythrocyte count, hemoglobin neutrophil C-reactive D-dimer levels) their threshold values. A intended predict built. Conclusion. obtained upon admission differ. Among them eleven associated development COVID-19. based on artificial intelligence method high accuracy predicts likelihood course adults.
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ژورنال
عنوان ژورنال: ?????? ????????????
سال: 2022
ISSN: ['2072-6732', '2499-9865']
DOI: https://doi.org/10.22625/2072-6732-2022-14-5-14-25