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 …

Background The promise of digital health is principally dependent on the ability to
electronically capture data that can be analyzed to improve decision-making. However, the …

Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

An increasing number of studies have reported using natural language processing (NLP) to
assist observational research by extracting clinical information from electronic health records …

The electronic health record (EHR) represents a rich source of patient information,
increasingly being leveraged for cardiovascular research. Although its primary use remains …

Building a high-quality annotation corpus requires expenditure of considerable time and
expertise, particularly for biomedical and clinical research applications. Most existing …

Introduction Falls are a leading cause of unintentional injury in the elderly. Electronic health
records (EHRs) offer the unique opportunity to develop models that can identify fall events …

Despite recent methodology advancements in clinical natural language processing (NLP),
the adoption of clinical NLP models within the translational research community remains …

Background Delirium is underdiagnosed in clinical practice and is not routinely coded for
billing. Manual chart review can be used to identify the occurrence of delirium; however, it is …

Background Covert cerebrovascular disease (CCD) has been shown to be associated with
dementia in population‐based studies with magnetic resonance imaging (MRI) screening …