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 …

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

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

In contemporary electronic medical records much of the clinically important data—signs and
symptoms, symptom severity, disease status, etc.—are not provided in structured data fields …

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 …

Background: Novel approaches that complement and go beyond evidence-based medicine
are required in the domain of chronic diseases, given the growing incidence of such …

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

Computerized clinical decision support (CDS) aims to aid decision making of health care
providers and the public by providing easily accessible health-related information at the …

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 …

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 …