Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.

ABSTRACT: COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature screenings, universal masking, isolation treatments) to control the spread of SARS-CoV-2 and mitigate outbreaks. We collect data from multiple sources, including demographics, clinical, treatment, laboratory, vaccination, socioeconomic status, and COVID-19 surveillance. Previous early prediction models, such as logistic regression, SVM, and XGBoost, require sophisticated feature engineering and need improved prediction performance. We create deep learning models, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (C

SUBMITTER: Xiao L 

PROVIDER: S-EPMC11464512 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature