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01000naa a22002652 4500 |
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NLM117123161 |
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DE-627 |
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20231222175831.0 |
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tu |
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231222s2001 xx ||||| 00| ||eng c |
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|a pubmed24n0391.xml
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|a (DE-627)NLM117123161
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|a (NLM)11825207
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Krauthammer, M
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries
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| 264 |
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1 |
|c 2001
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 24.05.2002
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| 500 |
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|a Date Revised 10.12.2019
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| 500 |
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|a published: Print
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| 500 |
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|a Citation Status MEDLINE
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| 520 |
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|a 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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| 650 |
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|a Evaluation Study
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, P.H.S.
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| 700 |
1 |
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|a Hripcsak, G
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (2001) vom: 11., Seite 339-43
|w (DE-627)NLM098642928
|x 1531-605X
|7 nnns
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| 773 |
1 |
8 |
|g year:2001
|g day:11
|g pages:339-43
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|a GBV_ILN_350
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| 951 |
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|a AR
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| 952 |
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|j 2001
|b 11
|h 339-43
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