Medical text representations for inductive learning
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 s...
| Veröffentlicht in: | Proceedings. AMIA Symposium. - 1998. - (2000) vom: 01., Seite 923-7 |
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| Format: | Aufsatz |
| Sprache: | English |
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2000
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| Zugriff auf das übergeordnete Werk: | Proceedings. AMIA Symposium |
| Schlagworte: | Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. |
| Zusammenfassung: | 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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| Beschreibung: | Date Completed 08.03.2001 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1531-605X |