Towards a comprehensive medical language processing system: methods and issues.
Natural language processing (NLP) systems can help solve the data entry problem by providing coded data from textual reports for clinical applications. A number of NLP systems have shown promise, but have not yet achieved wide-spread use for practical applications. In order to achieve such use, a system must have broad coverage of the clinical domain and not be restricted to limited applications. In addition, an NLP system must perform satisfactorily for real-world applications. This paper describes methods and issues associated with an ongoing extension of MedLEE, an operational NLP system, from a limited domain to a domain that encompasses comprehensive clinical information.
Friedman
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Department of Computer Science, Queens College CUNY, USA.
D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010351:Patient Discharge; D013902:Radiography, Thoracic / Q000145:classification; D011873:Radiology Information Systems