Impact of COVID‑19 pandemic restrictions and subsequent relaxation on the prevalence of respiratory virus hospitalizations in children.

Background: The COVID-19 pandemic and the consequently adopted worldwide control measures have resulted in global changes in the epidemiology and severity of other respiratory viruses. We compared the number and severity of viral acute lower respiratory tract infection (ALRTI) hospitalizations and determined changes in causative respiratory pathogens before, during, and after the pandemic among young children in Qatar.

Methods: In this single-center retrospective study, we reviewed data of children ≤ 36 months old who were admitted to Sidra Medicine in Qatar with a viral ALRTI during winter seasons (September-April) between 2019 and 2023.

Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions.

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study.

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection.

Automated Detection of Substance-Use Status and Related Information from Clinical Text.

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records.