Multi-label sentences (text) in the clinical domain result from the rich description of scenarios during patient care. The state-of-theart methods for assertion detection mostly address this task in the setting of a single assertion label per sentence (text). In addition, few rules based and deep learning methods perform negation/assertion scope detection on single-label text. It is a significant challenge extending these methods to address multi-label sentences without diminishing performance. Therefore, we developed a convolutional neural network (CNN) architecture to localize multiple labels and their scopes in a single stage end-to-end fashion, and demonstrate that our model performs atleast 12% better than the state-of-the-art on multi-label clinical text.
@article{arxiv.2005.09246,
title = {Assertion Detection in Multi-Label Clinical Text using Scope Localization},
author = {Rajeev Bhatt Ambati and Ahmed Ada Hanifi and Ramya Vunikili and Puneet Sharma and Oladimeji Farri},
journal= {arXiv preprint arXiv:2005.09246},
year = {2020}
}