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01000naa a22002652 4500 |
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NLM109955803 |
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DE-627 |
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20231222152440.0 |
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tu |
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231222s2000 xx ||||| 00| ||eng c |
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2 |
|a pubmed24n0367.xml
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|a (DE-627)NLM109955803
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|a (NLM)11080019
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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 Wilcox, A
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Medical text representations for inductive learning
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| 264 |
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1 |
|c 2000
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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 08.03.2001
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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 Inductive learning algorithms have been proposed as methods for classifying medical text reports. Many of these proposed techniques differ in the way the text is represented for use by the learning algorithms. Slight differences can occur between representations that may be chosen arbitrarily, but such differences can significantly affect classification algorithm performance. We examined 8 different data representation techniques used for medical text, and evaluated their use with standard machine learning algorithms. We measured the loss of classification-relevant information due to each representation. Representations that captured status information explicitly resulted in significantly better performance. Algorithm performance was dependent on subtle differences in data representation
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| 650 |
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|a Evaluation Study
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|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 (2000) vom: 01., Seite 923-7
|w (DE-627)NLM098642928
|x 1531-605X
|7 nnns
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| 773 |
1 |
8 |
|g year:2000
|g day:01
|g pages:923-7
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| 912 |
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|a GBV_USEFLAG_A
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| 912 |
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|a SYSFLAG_A
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| 912 |
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|a GBV_NLM
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| 912 |
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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 2000
|b 01
|h 923-7
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