[HTML][HTML] AMMU: a survey of transformer-based biomedical pretrained language models
Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …
natural language processing (NLP). These models combine the power of transformers …
Herpes zoster and simplex reactivation following COVID-19 vaccination: new insights from a vaccine adverse event reporting system (VAERS) database analysis
M Gringeri, V Battini, G Cammarata… - Expert Review of …, 2022 - Taylor & Francis
Background A few cases of Herpes Zoster and Simplex reactivation following COVID-19
immunization have been recently described, but the real extent of this suspected adverse …
immunization have been recently described, but the real extent of this suspected adverse …
Improving large language models for clinical named entity recognition via prompt engineering
Importance The study highlights the potential of large language models, specifically GPT-3.5
and GPT-4, in processing complex clinical data and extracting meaningful information with …
and GPT-4, in processing complex clinical data and extracting meaningful information with …
CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records
Objective Accurate extraction of breast cancer patients' phenotypes is important for clinical
decision support and clinical research. This study developed and evaluated cancer domain …
decision support and clinical research. This study developed and evaluated cancer domain …
AE-GPT: using large language models to extract adverse events from surveillance reports-a use case with influenza vaccine adverse events
Though Vaccines are instrumental in global health, mitigating infectious diseases and
pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large …
pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large …
Artificial intelligence based on machine learning in pharmacovigilance: a sco** review
Introduction Artificial intelligence based on machine learning has made large advancements
in many fields of science and medicine but its impact on pharmacovigilance is yet unclear …
in many fields of science and medicine but its impact on pharmacovigilance is yet unclear …
Large language models in biomedicine and health: current research landscape and future directions
Large language models (LLMs) are a specialized type of generative artificial intelligence (AI)
focused on generating natural language text. These models are developed through …
focused on generating natural language text. These models are developed through …
Historical profile will tell? A deep learning-based multi-level embedding framework for adverse drug event detection and extraction
L **a - Decision Support Systems, 2022 - Elsevier
Analyzing adverse drug events (ADEs) is an integral part of drug safety monitoring, which
plays a significant role in medication decision-making. The increasing prevalence of health …
plays a significant role in medication decision-making. The increasing prevalence of health …
Usefulness of vaccine adverse event reporting system for machine-learning based vaccine research: A Case study for COVID-19 vaccines
Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols
required for statistical analyses were pinpointed with a set of recommendations for the …
required for statistical analyses were pinpointed with a set of recommendations for the …
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
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 …
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …