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

The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Meaningful use of electronic health records (EHRs) for patient care or for research requires
data to be comparable. Many portions of EHRs continue to be unstructured, presenting …

Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

Electronic health record systems are ubiquitous and the majority of patients' data are now
being collected electronically in the form of free text. Deep learning has significantly …

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

One broad goal of biomedical informatics is to generate fully-synthetic, faithfully
representative electronic health records (EHRs) to facilitate data sharing between …