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 …

Despite improved ancillary investigations in epilepsy care, patients' narratives remain
indispensable for diagnosing and treatment monitoring. This wealth of information is …

Background Identifying key variables such as disorders within the clinical narratives in
electronic health records has wide-ranging applications within clinical practice and …

Objective To develop a natural language processing system that solves both clinical concept
extraction and relation extraction in a unified prompt-based machine reading …

Purpose Diagnosing monogenic diseases facilitates optimal care, but can involve the
manual evaluation of hundreds of genetic variants per case. Computational tools like Phrank …

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and
genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from …

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure
activity and improving patients' quality of life. Accurate evaluation, presurgical assessment …

Text and image analysis are playing very important role in healthcare and medical domain.
The whole clinical process is getting affected positively by text and image processing. Many …

Objective Routinely collected healthcare data are a powerful research resource but often
lack detailed disease-specific information that is collected in clinical free text, for example …

Natural language processing (NLP), a domain of artificial intelligence (AI) that models
human language, has been used in medicine to automate diagnostics, detect adverse …