Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

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 …

Named Entity Recognition (NER) is a key component in NLP systems for question
answering, information retrieval, relation extraction, etc. NER systems have been studied …

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 …

Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

Existing general clinical natural language processing (NLP) systems such as MetaMap and
Clinical Text Analysis and Knowledge Extraction System have been successfully applied to …

Transformers-based models, such as BERT, have dramatically improved the performance
for various natural language processing tasks. The clinical knowledge enriched model …