2018 (Track 2) - ADE & Medication Extraction

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