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 Artificial intelligence (AI), more specifically large language models (LLMs),
holds significant potential in revolutionizing emergency care delivery by optimizing clinical …

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending
the safety and benefit profiles of medicines. Although spontaneous reporting stays the …

Background Interest in the application of deep learning (DL) in critical care medicine (CCM)
is growing rapidly. However, comprehensive bibliometric research that analyze and …

Introduction: Adverse drug reactions (ADR) are directly related to public health and become
the focus of public and media attention. At present, a large number of ADR events have …

Online medical forums have emerged as vital platforms for patients to share their
experiences and seek advice, providing a valuable, cost-effective source of feedback for …

Patient safety is a key quality issue for health systems. Healthcare acquired adverse events
(AEs) compromise safety and quality; therefore, their reporting and monitoring is a patient …

Background Detecting Adverse Drug Events (ADEs) is an emerging research area, attracting
great interest in the research community. Better anticipatory management of predisposing …

In this paper, a tunable metasurface filter based on electrochemical metallization is
proposed. The finite element method is used to simulate the formation and rupture of the …

Deep learning, a subset of artificial intelligence (AI) that mimics the human brain's decision-
making process, has transformed several areas, from healthcare and finance to …