TY - GEN
T1 - Improving classification of medical assertions in clinical notes
AU - Kim, Youngjun
AU - Riloff, Ellen
AU - Meystre, Stéphane M.
PY - 2011
Y1 - 2011
N2 - We present an NLP system that classifies the assertion type of medical problems in clinical notes used for the Fourth i2b2/VA Challenge. Our classifier uses a variety of linguistic features, including lexical, syntactic, lexicosyntactic, and contextual features. To overcome an extremely unbalanced distribution of assertion types in the data set, we focused our efforts on adding features specifically to improve the performance of minority classes. As a result, our system reached 94.17% micro-averaged and 79.76% macro-averaged F1-measures, and showed substantial recall gains on the minority classes.
AB - We present an NLP system that classifies the assertion type of medical problems in clinical notes used for the Fourth i2b2/VA Challenge. Our classifier uses a variety of linguistic features, including lexical, syntactic, lexicosyntactic, and contextual features. To overcome an extremely unbalanced distribution of assertion types in the data set, we focused our efforts on adding features specifically to improve the performance of minority classes. As a result, our system reached 94.17% micro-averaged and 79.76% macro-averaged F1-measures, and showed substantial recall gains on the minority classes.
UR - https://www.scopus.com/pages/publications/84859062763
UR - https://www.scopus.com/pages/publications/84859062763#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:84859062763
SN - 9781932432886
T3 - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
SP - 311
EP - 316
BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Y2 - 19 June 2011 through 24 June 2011
ER -