Publications

2020
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
Alimova I, Tutubalina E. Multiple features for clinical relation extraction: A machine learning approach [Internet]. Journal of Biomedical Informatics 2020;103 https://doi.org/10.1016/j.jbi.2020.103382
See also: Post-challenge
2019
Li Y, Jin R, Luo Y. Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs) [Internet]. Journal of the American Medical Informatics Association 2019;26(3):262-268. https://doi.org/10.1093/jamia/ocy157
See also: Post-challenge
Li F, Yu H. An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models [Internet]. Journal of the American Medical Informatics Association 2019;26(7):646-654. https://doi.org/10.1093/jamia/ocz018
See also: Post-challenge
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings [Internet]. Journal of the American Medical Informatics Association 2019;26(11):1297-1304. https://doi.org/10.1093/jamia/ocz096
See also: Post-challenge
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

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