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Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries

Author

Listed:
  • Hui Xing Tan

    (Health Sciences Authority)

  • Chun Hwee Desmond Teo

    (Health Sciences Authority)

  • Pei San Ang

    (Health Sciences Authority)

  • Wei Ping Celine Loke

    (Health Sciences Authority)

  • Mun Yee Tham

    (Health Sciences Authority)

  • Siew Har Tan

    (Health Sciences Authority)

  • Bee Leng Sally Soh

    (Health Sciences Authority)

  • Pei Qin Belinda Foo

    (Health Sciences Authority)

  • Zheng Jye Ling

    (Regional Health System Office, National University of Singapore, National University Health System)

  • Wei Luen James Yip

    (National University Heart Centre
    National University Health System)

  • Yixuan Tang

    (National University of Singapore)

  • Jisong Yang

    (National University of Singapore)

  • Kum Hoe Anthony Tung

    (National University of Singapore)

  • Sreemanee Raaj Dorajoo

    (Health Sciences Authority)

Abstract

Introduction Discharge summaries contain valuable information about adverse drug reactions, but their unstructured nature makes them challenging to analyse and use as a signal source for pharmacovigilance. Machine learning has shown promise in identifying discharge summaries that contain related drug-adverse event pairs but has fared relatively poorer in entity extraction. Methods A hybrid model is developed combining rule-based and machine learning algorithms using discharge summaries with the aim of maximising capture of related drug-adverse event pairs. The rule first identifies segments containing adverse event entities within a 100-character distance from a drug term; machine learning subsequently estimates the relatedness of the drug and adverse event entities contained. The approach is validated on four independent datasets that are temporally and geographically separated from model development data. The impact of restricted drug-adverse event pair detection on recall is evaluated by using two of the four validation datasets that do not impose rule-based restrictions to annotations. Results The hybrid model achieves a recall of 0.80 (fivefold cross validation), 0.80 (temporal) and 0.76 (geographical) on validation using datasets containing only pre-identified target text segments that fulfil the rule-based algorithm criteria. When tested on datasets that additionally contained drug-adverse event pairs not restricted by the rule-based criteria, recall of the model declines to 0.68 and 0.62 on temporally and geographically separated datasets, respectively. Conclusions The proposed hybrid model demonstrates reasonable generalisability on external validation. Rule-based restriction of the detection space results in an approximately 12–14% reduction in recall but improves identification of the related drug and adverse event terms.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:drugsa:v:45:y:2022:i:8:d:10.1007_s40264-022-01196-x
    DOI: 10.1007/s40264-022-01196-x
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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. 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.
    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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