论文标题
使用范围定位的多标签临床文本中的断言检测
Assertion Detection in Multi-Label Clinical Text using Scope Localization
论文作者
论文摘要
临床领域中的多标签句子（文本）是由于对患者护理期间情景的丰富描述而产生的。主张检测的最先进的方法主要解决此任务，以每个句子的单个断言标签设置（文本）。此外，很少有基于规则和深度学习方法对单标签文本执行否定/断言范围检测。扩展这些方法来解决多标签句子而不降低性能，这是一个重大挑战。因此，我们开发了一个卷积神经网络（CNN）体系结构，以单阶段的端到端方式将多个标签及其范围定位，并证明我们的模型至少要比多标签临床文本上的先进的速度高12％。
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.
