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 
