Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text

BACKGROUND: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. METHODS: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. RESULTS: Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. CONCLUSIONS: This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.