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 …

Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et
al., 2018) have dramatically improved performance for many natural language processing …

A large array of pretrained models are available to the biomedical NLP (BioNLP) community.
Finding the best model for a particular task can be difficult and time-consuming. For many …

The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …

Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

Objective Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-
the-art results on clinical natural language processing (NLP) tasks. One of the core …

Objective This article summarizes the preparation, organization, evaluation, and results of
Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …