Generating better text summaries for a patient simulation program
| Autor: | D. Trace, Martha Evens, Kuo-pao Yang |
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| Rok vydání: | 2008 |
| Předmět: |
Vocabulary
SNOMED CT Generator (computer programming) Computer science business.industry media_common.quotation_subject Medical record computer.software_genre Variety (linguistics) Expert system Systematized Nomenclature of Medicine Component (UML) Artificial intelligence business computer Natural language processing media_common |
| Zdroj: | ACM Southeast Regional Conference |
| DOI: | 10.1145/1593105.1593120 |
| Popis: | We have been working on a new version of text generation component of the Intelligent Medical Record (IMR), a patient simulation program. The new text generator is based on American Bar Foundation (ABF) generation system to support the production of text for progress notes and for form letters. The old IMR produced a text report of patient encounters and also created a list of patient features, but this text contained a number of grammatical errors and disfluencies. The new ABF-based text generator runs on a variety of platforms, fixes the old generator errors, and incorporates standard vocabulary from the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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