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 …

Combating mental illnesses such as depression and anxiety has become a global concern.
As a result of the necessity for finding effective ways to battle these problems, machine …

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 …

We followed a systematic approach based on the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) …

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

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

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

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 …

Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

In the medical field, a doctor must have a comprehensive knowledge by reading and writing
narrative documents, and he is responsible for every decision he takes for patients …