Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

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 Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

Objective The goal of this study is to explore transformer-based models (eg, Bidirectional
Encoder Representations from Transformers [BERT]) for clinical concept extraction and …

The importance of incorporating Natural Language Processing (NLP) methods in clinical
informatics research has been increasingly recognized over the past years, and has led to …

Heart disease remains the major cause of death, despite recent improvements in prediction
and prevention. Risk factor identification is the main step in diagnosing and preventing heart …

One effective way to improve the state of the art is through competitions. Following the
success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics …

Abstract With Artificial Intelligence (AI) increasingly permeating various aspects of society,
including healthcare, the adoption of the Transformers neural network architecture is rapidly …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …