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Cascading Classifiers for Named Entity Recognition in Clinical Notes.

, and . BiomedicalIE@RANLP, page 42-49. Association for Computational Linguistics, (2009)

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Automatic Mapping Clinical Notes to Medical Terminologies., , and . ALTA, page 75-82. Australasian Language Technology Association, (2006)Cascading Classifiers for Named Entity Recognition in Clinical Notes., and . BiomedicalIE@RANLP, page 42-49. Association for Computational Linguistics, (2009)Mapping Clinical Notes to Medical Terminologies at Point of Care., and . BioNLP, page 102-103. Association for Computational Linguistics, (2008)An Automated System for Conversion of Clinical Notes into SNOMED Clinical Terminology., , and . ACSW, volume 68 of CRPIT, page 219-226. Australian Computer Society, (2007)A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT., , , and . BMC Medical Informatics Decis. Mak., 8 (S-1): S5 (2008)Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter., , , and . CoRR, (2021)Analyzing Social Media Data to Understand Consumer Information Needs on Dietary Supplements., , , , , , and . MedInfo, volume 264 of Studies in Health Technology and Informatics, page 323-327. IOS Press, (2019)Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling., , , and . MedInfo, volume 245 of Studies in Health Technology and Informatics, page 614-618. IOS Press, (2017)Detecting Signals of Associations Between Dietary Supplement Use and Mental Disorders from Twitter., , , and . ICHI Workshops, page 53-54. IEEE Computer Society, (2018)Analyzing Social Media Data to Understand Consumers' Information Needs on Dietary Supplements., , , , , , and . CoRR, (2019)