Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …

Natural language processing (NLP) is crucial for advancing healthcare because it is needed
to transform relevant information locked in text into structured data that can be used by …

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 …

Objective To provide a scoping review of papers on clinical natural language processing
(NLP) shared tasks that use publicly available electronic health record data from a cohort of …

Objectives: Owing to the rapid progress of natural language processing (NLP), the role of
NLP in the medical field has radically gained considerable attention from both NLP and …

Objective: Widespread application of clinical natural language processing (NLP) systems
requires taking existing NLP systems and adapting them to diverse and heterogeneous …

Objectives: We present a review of recent advances in clinical Natural Language Processing
(NLP), with a focus on semantic analysis and key subtasks that support such analysis …

After reading this chapter, you should know the answers to these questions: Why is natural
language processing important? What are the potential uses for natural language …

We followed a systematic approach based on the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) …

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 …