Uzuner Ö, Stubbs A, Lenert L. Advancing the state of the art in automatic extraction of adverse drug events from narratives [Internet]. Journal of the American Medical Informatics Association 2020;27(1) https://doi.org/10.1093/jamia/ocz206

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 [Internet]. Journal of the American Medical Informatics Association 2020;27(1):3-12. https://doi.org/10.1093/jamia/ocz166

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 [Internet]. Journal of the American Medical Informatics Association 2020;27(1):13-21. https://doi.org/10.1093/jamia/ocz063

Ju M, Nguyen NTH, Miwa M, Ananiadou S. An ensemble of neural models for nested adverse drug events and medication extraction with subwords [Internet]. Journal of the American Medical Informatics Association 2020;27(1):22-30. https://doi.org/10.1093/jamia/ocz075

Kim Y, Meystre SM. Ensemble method–based extraction of medication and related information from clinical texts [Internet]. Journal of the American Medical Informatics Association 2020;27(1):31-38. https://doi.org/10.1093/jamia/ocz100

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 [Internet]. Journal of the American Medical Informatics Association 2020;27(1):39-46. https://doi.org/10.1093/jamia/ocz101

Dai H-J, Su C-H, Wu C-S. Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings [Internet]. Journal of the American Medical Informatics Association 2020;27(1):47-55. 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 [Internet]. Journal of the American Medical Informatics Association 2020;27(1):56-64. https://doi.org/10.1093/jamia/ocz141

Yang X, Bian J, Fang R, Bjarnadottir RI, Hogan WR, Wu Y. Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting [Internet]. Journal of the American Medical Informatics Association 2020;27(1):65-72. https://doi.org/10.1093/jamia/ocz144

Stubbs A, Uzuner Ö. New approaches to cohort selection [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1161-1162. https://doi.org/10.1093/jamia/ocz174

Stubbs A, Filannino M, Soysal E, Henry S, Uzuner Ö. Cohort selection for clinical trials: n2c2 2018 shared task track 1 [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1163–1171. https://doi.org/10.1093/jamia/ocz163

Vydiswaran VGV, Strayhorn A, Zhao X, Robinson P, Agarwal M, Bagazinski E, Essiet M, Iott BE, Joo H, Ko PJ, Lee D, Lu JX, Liu J, Murali A, Sasagawa K, Wang T, Yuan N. Hybrid bag of approaches to characterize selection criteria for cohort identification [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1172-1180. https://doi.org/10.1093/jamia/ocz079

Segura-Bedmar I, Raez P. Cohort selection for clinical trials using deep learning models [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1181–1188. https://doi.org/10.1093/jamia/ocz139

Xiong Y, Shi X, Chen S, Jiang D, Tang B, Wang X, Chen Q, Yan J. Cohort selection for clinical trials using hierarchical neural network [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1203-1208. https://doi.org/10.1093/jamia/ocz099

Chen L, Gu Y, Ji X, Lou C, Sun Z, Li H, Gao Y, Huang Y. Clinical trial cohort selection based on multi-level rule-based natural language processing system [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1218–1226. https://doi.org/10.1093/jamia/ocz109

Chen C-J, Warikoo N, Chang Y-C, Chen J-H, Hsu W-L. Medical knowledge infused convolutional neural networks for cohort selection in clinical trials [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1227-1236. https://doi.org/10.1093/jamia/ocz128

Oleynik M, Kugic A, Kasáč Z, Kreuzthaler M. Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1247-1254. https://doi.org/10.1093/jamia/ocz149