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Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)

Author

Listed:
  • Abhyuday Jagannatha

    (University of Massachusetts)

  • Feifan Liu

    (University of Massachusetts Medical School)

  • Weisong Liu

    (University of Massachusetts
    University of Massachusetts Medical School)

  • Hong Yu

    (University of Massachusetts
    University of Massachusetts
    University of Massachusetts Medical School
    Bedford VAMC)

Abstract

Introduction This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 challenge for extracting medication, indication, and adverse drug events (ADEs) from electronic health record (EHR) notes. Objective The goal of MADE is to provide a set of common evaluation tasks to assess the state of the art for natural language processing (NLP) systems applied to EHRs supporting drug safety surveillance and pharmacovigilance. We also provide benchmarks on the MADE dataset using the system submissions received in the MADE 2018 challenge. Methods The MADE 1.0 challenge has released an expert-annotated cohort of medication and ADE information comprising 1089 fully de-identified longitudinal EHR notes from 21 randomly selected patients with cancer at the University of Massachusetts Memorial Hospital. Using this cohort as a benchmark, the MADE 1.0 challenge designed three shared NLP tasks. The named entity recognition (NER) task identifies medications and their attributes (dosage, route, duration, and frequency), indications, ADEs, and severity. The relation identification (RI) task identifies relations between the named entities: medication-indication, medication-ADE, and attribute relations. The third shared task (NER-RI) evaluates NLP models that perform the NER and RI tasks jointly. In total, 11 teams from four countries participated in at least one of the three shared tasks, and 41 system submissions were received in total. Results The best systems F1 scores for NER, RI, and NER-RI were 0.82, 0.86, and 0.61, respectively. Ensemble classifiers using the team submissions improved the performance further, with an F1 score of 0.85, 0.87, and 0.66 for the three tasks, respectively. Conclusion MADE results show that recent progress in NLP has led to remarkable improvements in NER and RI tasks for the clinical domain. However, some room for improvement remains, particularly in the NER-RI task.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:drugsa:v:42:y:2019:i:1:d:10.1007_s40264-018-0762-z
    DOI: 10.1007/s40264-018-0762-z
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    References listed on IDEAS

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    1. Alec B. Chapman & Kelly S. Peterson & Patrick R. Alba & Scott L. DuVall & Olga V. Patterson, 2019. "Detecting Adverse Drug Events with Rapidly Trained Classification Models," Drug Safety, Springer, vol. 42(1), pages 147-156, January.
    2. Bharath Dandala & Venkata Joopudi & Murthy Devarakonda, 2019. "Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks," Drug Safety, Springer, vol. 42(1), pages 135-146, January.
    3. 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.
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    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. Siun Kim & Taegwan Kang & Tae Kyu Chung & Yoona Choi & YeSol Hong & Kyomin Jung & Howard Lee, 2023. "Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques," Drug Safety, Springer, vol. 46(8), pages 781-795, August.
    4. Likeng Liang & Jifa Hu & Gang Sun & Na Hong & Ge Wu & Yuejun He & Yong Li & Tianyong Hao & Li Liu & Mengchun Gong, 2022. "Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources," Drug Safety, Springer, vol. 45(5), pages 511-519, May.

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