MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes
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Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs is that much of the detailed information is documented in a narrative manner. Clinical natural language processing (NLP) is the key technology to extract information from unstructured clinical text.
We present a machine learning-based clinical NLP system—MADEx—for detecting medications, ADEs, and their relations from clinical notes.
We developed a recurrent neural network (RNN) model using a long short-term memory (LSTM) strategy for clinical name entity recognition (NER) and compared it with baseline conditional random fields (CRFs). We also developed a modified training strategy for the RNN, which outperformed the widely used early stop strategy. For relation extraction, we compared support vector machines (SVMs) and random forests on single-sentence relations and cross-sentence relations. In addition, we developed an integrated pipeline to extract entities and relations together by combining RNNs and SVMs.
MADEx achieved the top-three best performances (F1 score of 0.8233) for clinical NER in the 2018 Medication and Adverse Drug Events (MADE1.0) challenge. The post-challenge evaluation showed that the relation extraction module and integrated pipeline (identify entity and relation together) of MADEx are comparable with the best systems developed in this challenge.
This study demonstrated the efficiency of deep learning methods for automatic extraction of medications, ADEs, and their relations from clinical text to support pharmacovigilance and drug safety surveillance.
The authors would like to thank the organizers who provided the annotated corpus and word embeddings for this challenge, and gratefully acknowledge the support of the NVIDIA Corporation with the donation of the GPUs used for this research. The authors would also like to thank the anonymous reviewers for their helpful feedback.
Compliance with Ethical Standards
This study was supported in part by the University of Florida Clinical and Translational Science Institute, which is funded by the National Institutes of Health (NIH) National Center for Advancing Translational Sciences under award number UL1TR001427, and the OneFlorida Clinical Research Consortium, which is funded by the Patient-Centered Outcomes Research Institute (PCORI) under award number CDRN-1501-26692. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Conflict of Interest
Xi Yang, Jiang Bian, Yan Gong, William R. Hogan, and Yonghui Wu have no conflicts of interest to declare that are directly relevant to the contents of this study.
This study utilized de-identified clinical notes provided by the University of Massachusetts Medical School through the MADE1.0 challenge, and was approved by the University of Florida Institutional Review Board.
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