This groundbreaking study explores the expanse of Large Language Models (LLMs), such
as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations …

Objective To provide a scoping review of papers on clinical natural language processing
(NLP) shared tasks that use publicly available electronic health record data from a cohort of …

Applying methods in natural language processing on electronic health records (EHR) data is
a growing field. Existing corpus and annotation focus on modeling textual features and …

Objective Generation of automated clinical notes has been posited as a strategy to mitigate
physician burnout. In particular, an automated narrative summary of a patient's hospital stay …

Medical decisions directly impact individuals' health and well-being. Extracting decision
spans from clinical notes plays a crucial role in understanding medical decision-making …

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020)
in note types such as radiology reports that typically have short length. In this work, we focus …

AI and NLP publication venues have increasingly encouraged researchers to reflect on
possible ethical considerations, adverse impacts, and other responsible AI issues their work …

We formulate and test a technique to use Emergent Communication (EC) with a pre-trained
multilingual model to improve on modern Unsupervised NMT systems, especially for low …

This work presents and evaluates an approach to efficiently leverage the context exploitation
ability of pre-trained Transformer models as a way of boosting the performance of models …

Physicians record their detailed thought-processes about diagnoses and treatments as
unstructured text in a section of a clinical note called the assessment and plan. This …