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20231222112457.0 |
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231222s1998 xx ||||| 00| ||eng c |
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|a pubmed24n0329.xml
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|a (DE-627)NLM098644017
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|a (NLM)9929283
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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100 |
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|a Zeng, Q
|e verfasserin
|4 aut
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|a Automated knowledge extraction from the UMLS
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|c 1998
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
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|2 rdacarrier
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|a Date Completed 16.03.1999
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|a Date Revised 13.11.2018
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|a published: Print
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|a Citation Status MEDLINE
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|a This paper presents our work in extracting disease-chemical relationship knowledge from the UMLS Co-occurrence table (MRCOC) using an automated method. We evaluated the quality of the knowledge from UMLS MRCOC by comparing it with knowledge from other sources: For disease-lab chemical relationships, knowledge was obtained from a decision support system (DXplain) and our own knowledge base of medical terminology (MED) through automated processes. For disease-drug chemical relationships, knowledge was manually acquired from the medical literature. Evaluations showed that the UMLS MRCOC knowledge has good sensitivity, especially regarding disease-drug relationships. We are using this knowledge to produce disease-specific views of patients' electronic patient record
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|a Journal Article
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|a Research Support, U.S. Gov't, P.H.S.
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|a Pharmaceutical Preparations
|2 NLM
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|a Cimino, J J
|e verfasserin
|4 aut
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|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (1998) vom: 13., Seite 568-72
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|x 1531-605X
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|j 1998
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