Published in:
Open Access 01-12-2019 | Research
Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text
Published in: BMC Medical Informatics and Decision Making | Special Issue 5/2019
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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.