Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input.
In this paper, we report our experience with the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0), which aims to promote deep innovations on this subject. In particular, we have developed rule-based sentence and word tokenization techniques to deal with the noise in the EHR text.
We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. We experiment with different word embedding methods commonly used in word-level classification tasks and demonstrate the impact of an integrated usage of both domain-specific and general-purpose pre-trained word embedding for detecting ADEs from EHRs.
Our system was ranked first for the named entity recognition task in the MADE1.0 challenge, with a micro-averaged F1-score of 0.8290 (official score).
Our results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes.
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We are grateful to Dr. Marni Hall (former Sr. Program Director), Suranjan De (Deputy Director), Sanjay K. Sahoo and Thang La, Regulatory Science, Office of Surveillance and Epidemiology (OSE), US Food and Drug Administration (FDA) for introducing us to pharmacovigilance in general and the ADE detection problem in particular. We also thank Worcester Polytechnic Institute (WPI) Data Science Research Group (DSRG) members for their valuable feedback.
Conflict of interest
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Cansu Sen, Elke A. Rundensteiner and Xiangnan Kong have no conflicts of interest that are directly relevant to the content of this study.
Susmitha Wunnava is thankful to the Seeds of STEM and Institute of Education Sciences, US Department of Education for supporting her PhD studies via the Grant R305A150571. Xiao and Tabassum are grateful to Oak Ridge Associated Universities (ORAU) for granting them an ORISE Fellowship to conduct research with the US Food and Drug Administration.
Part of a theme issue on “NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0)” guest edited by Feifan Liu, Abhyuday Jagannatha and Hong Yu.
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Wunnava, S., Qin, X., Kakar, T. et al. Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding. Drug Saf 42, 113–122 (2019). https://doi.org/10.1007/s40264-018-0765-9