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 …

Context: Although patient safety is a major problem, most health care organizations rely on
spontaneous reporting, which detects only a small minority of adverse events. As a result …

For single databases, primary hindrances for end-user access are the volume of data that is
becoming available, the lack of abstraction, and the need to understand the representation …

Medication information is one of the most important types of clinical data in electronic
medical records. It is critical for healthcare safety and quality, as well as for clinical research …

Interoperability is one of the hottest topics in healthcare, yet one of the least well understood.
Successful interoperability offers great opportunities to improve quality and outcomes while …

An information extraction system that allows users to ask questions about documents in a
database, and responds to queries by returning possibly relevant information which is …

This volume takes a broad view of information extraction as any method for filtering
information from large volumes of text. This includes the retrieval of documents from …

This series is directed to healthcare professionals who are leading the transfor-tion of health
care by using information and knowledge to advance the quality of patient care. Launched in …

Objective The authors' goal was to develop and evaluate machine-learning-based
approaches to extracting clinical entities—including medical problems, tests, and treatments …

This volume brings together ontology learning, knowledge acquisition and other related
topics. It presents current research in ontology learning, addressing three perspectives. The …