An Introduction to Natural Language Processing. Issue 7 (July 2015)

• Record Type: Journal Article
• Authors: Kimia, Amir A.; Savova, Guergana; Landschaft, Assaf; Harper, Marvin B.
• Is Part Of: Pediatric emergency care. Volume 31:Issue 7(2015)
• Publication Date: 2015-07
• Subjects: Pediatric emergencies -- Periodicals; 618.92002505
• DOI: 10.1097/PEC.0000000000000484
• Languages: English
• ISSNs: 0749-5161

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <title>Abstract</title> <p>Electronically stored clinical documents may contain both structured data and unstructured data. The use of structured clinical data varies by facility, but clinicians are familiar with coded data such as <italic>International</italic><italic>Classification of Diseases, Ninth Revision</italic>, Systematized Nomenclature of Medicine—Clinical Terms codes, and commonly other data including patient chief complaints or laboratory results. Most electronic health records have much more clinical information stored as unstructured data, for example, clinical narrative such as history of present illness, procedure notes, and clinical decision making are stored as unstructured data. Despite the importance of this information, electronic capture or retrieval of unstructured clinical data has been challenging. The field of natural language processing (NLP) is undergoing rapid development, and existing tools can be successfully used for quality improvement, research, healthcare coding, and even billing compliance. In this brief review, we provide examples of successful uses of NLP using emergency medicine physician visit notes for various projects and the challenges of retrieving specific data and finally present practical methods that can run on a standard personal computer as well as high-end state-of-the-art funded processes run by leading NLP informatics researchers.</p> </sec> </abstract>