Automatic identification of clinical trials for which a patient is eligi...
This paper investigates the use of artificial intelligence chatbots for
...
We introduce an annotated corpus of 600 ophthalmology notes labeled with...
Model card reports provide a transparent description of machine learning...
Clinical semantic parsing (SP) is an important step toward identifying t...
This paper develops the first question answering dataset (DrugEHRQA)
con...
We present an overview of the TREC-COVID Challenge, an information retri...
We present the Hierarchical Transformer Networks for modeling long-term
...
The paradigm of representation learning through transfer learning has th...
Classifying fine-grained ischemic stroke phenotypes relies on identifyin...
Patient representation learning refers to learning a dense mathematical
...
This paper describes an initial dataset and automatic natural language
p...
Radiology reports have been widely used for extraction of various clinic...
We apply deep learning-based language models to the task of patient coho...
The COVID-19 pandemic has resulted in a tremendous need for access to th...
TREC-COVID is a community evaluation designed to build a test collection...
We define a representation framework for extracting spatial information ...
Objective: There is a lot of information about cancer in Electronic Heal...
Neural network-based representations ("embeddings") have dramatically
ad...
Despite the recent advances in automatically describing image contents, ...