Objective Natural language processing (NLP) of symptoms from electronic health records
(EHRs) could contribute to the advancement of symptom science. We aim to synthesize the …

Background: Clinical narratives represent the main form of communication within health
care, providing a personalized account of patient history and assessments, and offering rich …

Research has shown that accounting for moral sentiment in natural language can yield
insight into a variety of on-and off-line phenomena such as message diffusion, protest …

Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …

Depression is a common illness worldwide with doubtless severe implications. Due to the
absence of early identification and treatment for depression, millions of individuals …

Objective To evaluate the prevalence of seven social factors using physician notes as
compared to claims and structured electronic health records (EHR s) data and the resulting …

The pervasive use of electronic health databases has increased the accessibility of free-text
clinical reports for supplementary use. Several text classification approaches, such as …

Objective Natural language processing (NLP) combined with machine learning (ML)
techniques are increasingly used to process unstructured/free-text patient-reported outcome …

In recent years, the evolution of technology has led to an increase in text data obtained from
many sources. In the biomedical domain, text information has also evidenced this …

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 …