In this paper, we propose a novel approach for clinical name entity recognition based on deep machine learning architecture. The proposed scheme based on two different deep learning architectures: the feed forward networks (FFN), and the recurrent neural network (RNN), allow significant improvement in performance, in terms of different performance measures, including precision, recall and F-score, when evaluated with the CLEF 2016 Challenge task 1 A dataset corresponding to Clinical Nursing Handover. It was possible to achieve an F-score of 66% with RNN architecture, which was higher than most of the other participating systems in the Challenge task.
Luu, T. M., Phan, R., Davey, R., & Chetty, G. (2018). Clinical Name Entity Recognition Based on Recurrent Neural Networks. In Proceedings of the 2018 18th International Conference on Computational Science and Its Applications, ICCSA 2018. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICCSA.2018.8439147