We followed a systematic approach based on the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) …

Importance Natural language processing (NLP) has the potential to accelerate translation of
cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of …

A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …

Advances in genotyping technology have, over the past decade, enabled the focused
search for common genetic variation associated with human diseases and traits. With the …

Abstract Background The World Health Organization (WHO) and the International Labour
Organization (ILO) are developing joint estimates of the work-related burden of disease and …

Electronic health record systems are ubiquitous and the majority of patients' data are now
being collected electronically in the form of free text. Deep learning has significantly …

Electronic medical records are increasingly used to store patient information in hospitals and
other clinical settings. There has been a corresponding proliferation of clinical natural …

Background Free-text descriptions in electronic health records (EHRs) can be of interest for
clinical research and care optimization. However, free text cannot be readily interpreted by a …

Objective To summarize applications of natural language processing (NLP) in model
informed drug development (MIDD) and identify potential areas of improvement. Materials …

Objective This article presents our approaches to extraction of medications and associated
adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 …