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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Bibliographic Details
Published inWuhan University journal of natural sciences Vol. 21; no. 6; pp. 482 - 490
Main Author Lü Chen CHEN Bo Lü Chaozhen QIU Likun JI Donghong
Format Journal Article
LanguageEnglish
Published Wuhan Wuhan University 01.12.2016
Springer Nature B.V
Subjects
Biomedical and Life Sciences
Classification
Computer Science
Life Sciences
Materials Science
Mathematical analysis
Similarity
Levenshtein distance
TP391
natural language processing
disorder normalization
semantic composition
multiple features
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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.
Bibliography: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.
42-1405/N
natural language processing disorder normalization Levenshtein distance semantic composition multiple features
ISSN:1007-1202
1993-4998
DOI:10.1007/s11859-016-1200-7