A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries
At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressi...
| Veröffentlicht in: | Proceedings. AMIA Symposium. - 1998. - (2001) vom: 11., Seite 339-43 |
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| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2001
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| Zugriff auf das übergeordnete Werk: | Proceedings. AMIA Symposium |
| Schlagworte: | Evaluation Study Journal Article Research Support, U.S. Gov't, P.H.S. |
| Zusammenfassung: | At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressions by the use of concept modifiers (like change, certainty, degree and status). As a tradeoff, extraction of application-specific medical information is difficult without a clear understanding of how these modifiers combine. We report on a knowledge model for MedLEE modifiers that is helpful for a high level interpretation of NLP data and is used for the generation of two distinct views on NLP-parsed discharge summaries: A physician view offering a condensed overview of the severity of patient problems and a data mining view featuring binary problem states useful for machine learning |
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| Beschreibung: | Date Completed 24.05.2002 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1531-605X |