Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Understanding a patient's medical history, such as how long symptoms last or when a
procedure was performed, is vital to diagnosing problems and providing good care …

There has been a steady need in the medical community to precisely extract the temporal
relations between clinical events. In particular, temporal information can facilitate a variety of …

Transformer-based neural language models have led to breakthroughs for a variety of
natural language processing (NLP) tasks. However, most models are pretrained on general …

Every day, enormous amounts of biomedical texts discussing various biomedical topics are
produced. Revealing strong semantic connections hidden in those unstructured data is …

Biomedical relation extraction (RE) is the task of automatically identifying and characterizing
relations between biomedical concepts from free text. RE is a central task in biomedical …

Deep neural networks have the capacity to generate textual content which is increasingly
difficult to distinguish from that produced by humans. Such content can be used in …

Textual documents serve as representations of discussions on a variety of subjects. These
discussions can vary in length and may encompass a range of events or factual information …

Objectives: We survey recent work in biomedical NLP on building more adaptable or
generalizable models, with a focus on work dealing with electronic health record (EHR) …

Unstructured data in electronic health records, represented by clinical texts, are a vast
source of healthcare information because they describe a patient's journey, including clinical …