Assertion Detection in Multi-Label Clinical Text using Scope Localization

• Main Authors: Ambati, Rajeev Bhatt, Hanifi, Ahmed Ada, Vunikili, Ramya, Sharma, Puneet, Farri, Oladimeji
• Format: Journal Article
• Language: English
• Published: 19-05-2020
• Subjects: Computer Science - Computation and Language; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2005.09246

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.