Detect Attributes of Medical Concepts via Sequence Labeling
Jun Xu, Zhiheng Li, Qiang Wei, Yonghui Wu, Yang Xiang, Heejin Lee, Yaoyun Zhang, Stephen Wu, Hua Xu · 2019
In this study, we present a new method for detecting attributes of medical concepts, which uses a sequence labeling approach to recognize attribute entities and classify relations between concepts and attributes simultaneously within one step. A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect disorder-modifier pairs in clinical text. Evaluations on the ShARe corpus show that the proposed method achieved higher accuracy and F1 scores than the traditional two-step approaches, indicating its potential to accelerate practical clinical NLP applications.