Currently, medical institutes generally use EMR to record patient's condition, including
diagnostic information, procedures performed, and treatment results. EMR has been …

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 …

Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

Objective Medical knowledge graph (KG) is attracting attention from both academic and
healthcare industry due to its power in intelligent healthcare applications. In this paper, we …

Background Automatic clinical text classification is a natural language processing (NLP)
technology that unlocks information embedded in clinical narratives. Machine learning …

Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural
Language Processing (NLP) systems, which aims to recognize and classify clinical entities …

Background Entity recognition is one of the most primary steps for text analysis and has long
attracted considerable attention from researchers. In the clinical domain, various types of …

De-identification, identifying information from data, such as protected health information
(PHI) present in clinical data, is a critical step to enable data to be shared or published. The …

Rapid growth in electronic health records (EHRs) use has led to an unprecedented
expansion of available clinical data in electronic formats. However, much of the important …

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