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A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Orthopedics* ; Malpractice*; Humans ; Reproducibility of Results ; China ; Machine Learning
    • Abstract:
      Competing Interests: The authors have declared that no competing interests exist.
      Purpose: To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions.
      Methods: We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models.
      Results: We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%.
      Conclusions: The optimal model of this study is expected to predict the causality accurately.
      (Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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    • Publication Date:
      Date Created: 20240417 Date Completed: 20240419 Latest Revision: 20240419
    • Publication Date:
      20250114
    • Accession Number:
      PMC11023448
    • Accession Number:
      10.1371/journal.pone.0300662
    • Accession Number:
      38630758