Epidemiology of infection at a tertiary care facility in Saudi Arabia: Results of prospective surveillance.

Objectives: To determine the incidence of infection (CDI) and the frequency of known risk factors.

Methods: A prospective hospital-based surveillance for CDI, according to the Centers for Disease Control and Prevention criteria, was carried out from July 2019 to March 2022 for all inpatients aged more than one year in Prince Sultan Military Medical City, Riyadh, Saudi Arabia.

Results: A total of 139 cases of CDI were identified during the survey among 130 patients admitted in the hospital.

Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype.

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients' LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS.

Children's glycemic control: mother's knowledge and socioeconomic status.

The present study was designed to examine the role of socioeconomic status (SES) of the mother's knowledge about different aspects of diabetes and the glycemic control of type 1 children with diabetes. Samples were taken from successive admissions to the outpatient diabetes clinics in Prince Sultan Medical Military City (PSMMC), Riyadh, Saudi Arabia. A well designed questionnaire covering different aspects including demographic data, educational background, and socioeconomic status of the care providers was used to collect information from mothers of type 1 diabetes mellitus (T1DM) children.