Introduction Pharmacovigilance programs protect patient health and safety by identifying
adverse event signals through postmarketing surveillance of claims data and spontaneous …

Introduction Pharmacovigilance (PV) involves monitoring and aggregating adverse event
information from a variety of data sources, including health records, biomedical literature …

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 Machine learning can operationalize the rich and complex data in electronic
patient records for exploratory pharmacovigilance endeavours. Objective The objective of …

Objectives: To analyse the content of publications within the medical Natural Language
Processing (NLP) domain in 2022. Methods: Automatic and manual preselection of …

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in
drug discovery and development (DDD), revolutionizing many aspects of the process. This …

Extracting mentions of adverse drug events and relationships between them is crucial for
effective pharmacovigilance and drug safety surveillance. Recently, transformer-based …

This PhD dissertation focuses on the development of language technology that can be used
to extract clinical information from Danish electronic health records (EHRs). EHRs contain …