A broad-coverage natural language processing system
Natural language processing systems (NLP) that extract clinical information from textual reports were shown to be effective for limited domains and for particular applications. Because an NLP system typically requires substantial resources to develop, it is beneficial if it is designed to be easily...
| Veröffentlicht in: | Proceedings. AMIA Symposium. - 1998. - (2000) vom: 01., Seite 270-4 |
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| 1. Verfasser: | |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2000
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| Zugriff auf das übergeordnete Werk: | Proceedings. AMIA Symposium |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. |
| Zusammenfassung: | Natural language processing systems (NLP) that extract clinical information from textual reports were shown to be effective for limited domains and for particular applications. Because an NLP system typically requires substantial resources to develop, it is beneficial if it is designed to be easily extendible to multiple domains and applications. This paper describes multiple extensions of an NLP system called MedLEE, which was originally developed for the domain of radiological reports of the chest, but has subsequently been extended to mammography, discharge summaries, all of radiology, electrocardiography, echocardiography, and pathology |
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| Beschreibung: | Date Completed 08.03.2001 Date Revised 11.03.2022 published: Print Citation Status MEDLINE |
| ISSN: | 1531-605X |