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| Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF |
Xiaoxiao Zhu,Zunqi Yang,Jing Liu( ) |
| Department of Management Information System, Tianjin University of Finance and Economics,Tianjin 300222, China |
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Abstract [Objective] To improve the performance of extracting adverse drug reactions from social media, a method is proposed to deal with non-standard texts in social media. [Methods] This method Bi-LSTM-CRF combined LSTM and CRF, and was implemented using TensorFlow. LSTM Could utilize context information, while CRF Could consider the dependence of output tags. An adverse drug reaction extraction model was constructed based on Bi-LSTM-CRF. [Results] A series of experiments were carried out on the Twitter dataset. The experimental results showed that the proposed Bi-LSTM-CRF method achieved the highest F-measure (0.7963) for adverse drug reaction extraction, compared with other methods, including CRF, forward LSTM, backward LSTM, and Bi-LSTM. [Limitations] The experiments were performed on only one corpus, and the validity of the proposed method need be verified on other data sources. [Conclusions] Combining Bi-LSTM and CRF can effectively deal with non-standard texts in social media. The constructed model in this paper can identify adverse drug reactions effectively and support relevant departments in decision-making.
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Received: 04 June 2018
Published: 27 March 2019
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