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arXiv · 2005.09246

Assertion Detection in Multi-Label Clinical Text using Scope Localization

Rajeev Bhatt Ambati·Ahmed Ada Hanifi·Ramya Vunikili·Puneet Sharma·Oladimeji Farri
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Abstract

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.

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BibTeXRIS

Rajeev Bhatt Ambati, Ahmed Ada Hanifi, Ramya Vunikili, Puneet Sharma, Oladimeji Farri. 2020-05-19. Assertion Detection in Multi-Label Clinical Text using Scope Localization. https://arxiv.org/abs/2005.09246

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