A Multiple Feature Approach for Disorder Normalization in Clinical Notes
In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system(UMLS)concept unique identifier(CUI). We develop a two-step method to acquire a list of candidate CUIs and their associated prefe...
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Published in | 武汉大学学报:自然科学英文版 no. 6; pp. 482 - 490 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
2016
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Online Access | Get full text |
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Summary: | In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system(UMLS)concept unique identifier(CUI). We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features(string features,ranking features,similarity features,and contextual features) are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline. |
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Bibliography: | Lü Chen;CHEN Bo;Lü Chaozhen;QIU Likun;JI Donghong;School of Computer,Wuhan University;Department of Chinese Language and Literature,Hubei University of Art and Science;Shandong Key Lab of Language Resource Development and Application,Ludong University 42-1405/N |
ISSN: | 1007-1202 1993-4998 |