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 d...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.展开更多
基金Supported by the National Natural Science Foundation of China(61133012,61202193,61373108)the Major Projects of the National Social Science Foundation of China(11&ZD189)+1 种基金the Chinese Postdoctoral Science Foundation(2013M540593,2014T70722)the Open Foundation of Shandong Key Laboratory of Language Resource Development and Application
文摘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.
