With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the
medical and healthcare fields are utilizing machine learning (ML) methodologies in …

This comprehensive overview explores the integration of machine learning (ML) in data
warehousing, focusing on optimization challenges, methodologies, results, and future …

Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …

Background A novel disease poses special challenges for informatics solutions. Biomedical
informatics relies for the most part on structured data, which require a preexisting data or …

Introduction Chlorpromazine has been suggested as being potentially useful in patients with
coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti …

Objective To examine the association between antidepressant use and the risk of intubation
or death in hospitalized patients with COVID-19. Design Multicenter observational …

Aims To examine the association between dexamethasone use and mortality among
patients hospitalized for COVID‐19. Methods We examined the association between …

Background In recent years, health data collected during the clinical care process have
been often repurposed for secondary use through clinical data warehouses (CDWs), which …

Background and objectives The importance of clinical natural language processing (NLP)
has increased with the adoption of electronic health records (EHRs). One of the critical tasks …

Background Previous studies have shown mixed evidence on ethnic disparities in
antipsychotic prescribing among patients with psychosis in the UK, partly due to small …