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MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes

Author

Listed:
  • Xi Yang

    (University of Florida)

  • Jiang Bian

    (University of Florida)

  • Yan Gong

    (University of Florida)

  • William R. Hogan

    (University of Florida)

  • Yonghui Wu

    (University of Florida)

Abstract

Introduction 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. Objective We present a machine learning-based clinical NLP system—MADEx—for detecting medications, ADEs, and their relations from clinical notes. Methods 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. Results 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. Conclusion 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.

Suggested Citation

  • Xi Yang & Jiang Bian & Yan Gong & William R. Hogan & Yonghui Wu, 2019. "MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes," Drug Safety, Springer, vol. 42(1), pages 123-133, January.
  • Handle: RePEc:spr:drugsa:v:42:y:2019:i:1:d:10.1007_s40264-018-0761-0
    DOI: 10.1007/s40264-018-0761-0
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    Cited by:

    1. Feifan Liu & Abhyuday Jagannatha & Hong Yu, 2019. "Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records," Drug Safety, Springer, vol. 42(1), pages 95-97, January.
    2. Hui Xing Tan & Chun Hwee Desmond Teo & Pei San Ang & Wei Ping Celine Loke & Mun Yee Tham & Siew Har Tan & Bee Leng Sally Soh & Pei Qin Belinda Foo & Zheng Jye Ling & Wei Luen James Yip & Yixuan Tang &, 2022. "Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries," Drug Safety, Springer, vol. 45(8), pages 853-862, August.
    3. Abhyuday Jagannatha & Feifan Liu & Weisong Liu & Hong Yu, 2019. "Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)," Drug Safety, Springer, vol. 42(1), pages 99-111, January.
    4. Heba Edrees & Wenyu Song & Ania Syrowatka & Aurélien Simona & Mary G. Amato & David W. Bates, 2022. "Intelligent Telehealth in Pharmacovigilance: A Future Perspective," Drug Safety, Springer, vol. 45(5), pages 449-458, May.

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