# Publications Sort & Filters close ## Filters Challenges expand\_more - 2006 - Deidentification & Smoking (9) - 2008 - Obesity (8) - 2009 - Medication (12) - 2010 - Relations (11) - 2011 - Coreference (8) - 2012 - Temporal Relations (17) - 2014 - Deidentification & Heart Disease (27) - 2016 - RDoC for Psychiatry (17) - expand\_moreexpand\_less2018 (2 Tracks) (17) - 2018 (Track 1) - Clinical Trial Cohort Selection (8) - 2018 (Track 2) - ADE & Medication Extraction (9) - Post-challenge (15) Search Within Results Search Within Results search ## 141 results Show filters filter\_alt Sort by Year of PublicationAlphabetical A-Z sort ## 141 results Download 141 citations download- [BibTeX](/node/1655891/export?format=bibtex) - [EndNote X3 XML](/node/1655891/export?format=endnote8) - [EndNote 7 XML](/node/1655891/export?format=endnote7) - [Endnote tagged](/node/1655891/export?format=tagged) - [Marc](/node/1655891/export?format=marc) - [PubMedId](/node/1655891/export?format=pubmed_id) - [RIS](/node/1655891/export?format=ris) ### 2020 Henry S, Buchan K, Filannino M, Stubbs A, Uzuner Ö. [2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records](/publications/2018-n2c2-shared-task-adverse-drug-events-and-medication-extraction-electronic). Journal of the American Medical Informatics Association. 2020;27(1):3–12. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner Ö. [2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records](/publications/2018-n2c2-shared-task-adverse-drug-events-and-medication-extraction-electronic). Journal of the American Medical Informatics Association. 2020;27(1):3–12. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz166) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz166) Alimova I, Tutubalina E. [Multiple features for clinical relation extraction: A machine learning approach](/publications/multiple-features-clinical-relation-extraction-machine-learning-approach). Journal of Biomedical Informatics. 2020;103. Alimova I, Tutubalina E. [Multiple features for clinical relation extraction: A machine learning approach](/publications/multiple-features-clinical-relation-extraction-machine-learning-approach). Journal of Biomedical Informatics. 2020;103. - [ descriptionPublisher's Version](https://doi.org/10.1016/j.jbi.2020.103382) - [ descriptionPublisher's Version](https://doi.org/10.1016/j.jbi.2020.103382) Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. [A study of deep learning approaches for medication and adverse drug event extraction from clinical text](/publications/study-deep-learning-approaches-medication-and-adverse-drug-event-extraction). Journal of the American Medical Informatics Association. 2020;27(1):13–21. Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. [A study of deep learning approaches for medication and adverse drug event extraction from clinical text](/publications/study-deep-learning-approaches-medication-and-adverse-drug-event-extraction). Journal of the American Medical Informatics Association. 2020;27(1):13–21. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz063) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz063) Yang X, Bian J, Fang R, Bjarnadottir R, Hogan W, Wu Y. [Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting](/publications/identifying-relations-medications-adverse-drug-events-using-recurrent). Journal of the American Medical Informatics Association. 2020;27(1):65–72. Yang X, Bian J, Fang R, Bjarnadottir R, Hogan W, Wu Y. [Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting](/publications/identifying-relations-medications-adverse-drug-events-using-recurrent). Journal of the American Medical Informatics Association. 2020;27(1):65–72. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz144) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz144) Kim Y, Meystre S. [Ensemble method–based extraction of medication and related information from clinical texts](/publications/ensemble-method%E2%80%93based-extraction-medication-and-related-information-clinical-texts). Journal of the American Medical Informatics Association. 2020;27(1):31–38. Kim Y, Meystre S. [Ensemble method–based extraction of medication and related information from clinical texts](/publications/ensemble-method%E2%80%93based-extraction-medication-and-related-information-clinical-texts). Journal of the American Medical Informatics Association. 2020;27(1):31–38. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz100) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz100) Ju M, Nguyen N, Miwa M, Ananiadou S. [An ensemble of neural models for nested adverse drug events and medication extraction with subwords](/publications/ensemble-neural-models-nested-adverse-drug-events-and-medication-extraction). Journal of the American Medical Informatics Association. 2020;27(1):22–30. Ju M, Nguyen N, Miwa M, Ananiadou S. [An ensemble of neural models for nested adverse drug events and medication extraction with subwords](/publications/ensemble-neural-models-nested-adverse-drug-events-and-medication-extraction). Journal of the American Medical Informatics Association. 2020;27(1):22–30. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz075) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz075) Christopoulou F, Tran TT, Sahu SK, Miwa M, Ananiadou S. [Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods](/publications/adverse-drug-events-and-medication-relation-extraction-electronic-health-records). Journal of the American Medical Informatics Association. 2020;27(1):39–46. Christopoulou F, Tran TT, Sahu SK, Miwa M, Ananiadou S. [Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods](/publications/adverse-drug-events-and-medication-relation-extraction-electronic-health-records). Journal of the American Medical Informatics Association. 2020;27(1):39–46. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz101) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz101) Uzuner Ö, Stubbs A, Lenert L. [Advancing the state of the art in automatic extraction of adverse drug events from narratives](/publications/advancing-state-art-automatic-extraction-adverse-drug-events-narratives). Journal of the American Medical Informatics Association. 2020;27(1). Uzuner Ö, Stubbs A, Lenert L. [Advancing the state of the art in automatic extraction of adverse drug events from narratives](/publications/advancing-state-art-automatic-extraction-adverse-drug-events-narratives). Journal of the American Medical Informatics Association. 2020;27(1). - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz206) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz206) Dai HJ, Su CH, Wu CS. [Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings](/publications/adverse-drug-event-and-medication-extraction-electronic-health-records-cascading). Journal of the American Medical Informatics Association. 2020;27(1):47–55. Dai HJ, Su CH, Wu CS. [Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings](/publications/adverse-drug-event-and-medication-extraction-electronic-health-records-cascading). Journal of the American Medical Informatics Association. 2020;27(1):47–55. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz120) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz120) Chen L, Gu Y, Ji X, Sun Z, Li H, Gao Y, Huang Y. [Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning](/publications/extracting-medications-and-associated-adverse-drug-events-using-natural-language). Journal of the American Medical Informatics Association. 2020;27(1):56–64. Chen L, Gu Y, Ji X, Sun Z, Li H, Gao Y, Huang Y. [Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning](/publications/extracting-medications-and-associated-adverse-drug-events-using-natural-language). Journal of the American Medical Informatics Association. 2020;27(1):56–64. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz141) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz141) ### 2019 Oleynik M, Kugic A, Kasáč Z, Kreuzthaler M. [Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification](/publications/evaluating-shallow-and-deep-learning-strategies-2018-n2c2-shared-task-clinical). Journal of the American Medical Informatics Association. 2019;26(11):1247–1254. Oleynik M, Kugic A, Kasáč Z, Kreuzthaler M. [Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification](/publications/evaluating-shallow-and-deep-learning-strategies-2018-n2c2-shared-task-clinical). Journal of the American Medical Informatics Association. 2019;26(11):1247–1254. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz149) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz149) Stubbs A, Filannino M, Soysal E, Henry S, Uzuner Ö. [Cohort selection for clinical trials: n2c2 2018 shared task track 1](/publications/cohort-selection-clinical-trials-n2c2-2018-shared-task-track-1). Journal of the American Medical Informatics Association. 2019;26(11):1163–1171. Stubbs A, Filannino M, Soysal E, Henry S, Uzuner Ö. [Cohort selection for clinical trials: n2c2 2018 shared task track 1](/publications/cohort-selection-clinical-trials-n2c2-2018-shared-task-track-1). Journal of the American Medical Informatics Association. 2019;26(11):1163–1171. - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz163) - [ descriptionPublisher's Version](https://doi.org/10.1093/jamia/ocz163) - Previous page chevron\_left - [1](?page=0 "Current page") - [2](?page=1 "Go to page 2") - [3](?page=2 "Go to page 3") - [ Last page 12 ](?page=11 "Go to last page") - [ Next page chevron\_right ](?page=1 "Go to next page")