In secondary analysis of electronic health records, a crucial task consists in correctly
identifying the patient cohort under investigation. In many cases, the most valuable and …

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 …

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives
constitutes a basic enabling technology to unlock the knowledge within and support more …

Artificial intelligence provides the opportunity to reveal important information buried in large
amounts of complex data. Electronic health records (eHRs) are a source of such big data …

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 …

Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve
population-scale phenotyping for clinical and genetic research. Currently, selection of text …

Electronic Health Records (EHR) are a rich repository of valuable clinical information that
exist in primary and secondary care databases. In order to utilize EHRs for medical …

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

Background The medical subdomain of a clinical note, such as cardiology or neurology, is
useful content-derived metadata for developing machine learning downstream applications …