ArticleHealthcare (Basel, Switzerland)2022
Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Development and Validation of Machine Learning Models for Postjudgment Estimation of High Compensation Ratios After Lower Limb Fracture Surgery: Retrospective Study.JMIR medical informatics · 2026Article
- Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.BMC geriatrics · 2025Article
- A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.PloS one · 2024Article
- Trends and Developments in Medical Liability Claims in The Netherlands.Healthcare (Basel, Switzerland) · 2022Article
- Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images.Healthcare (Basel, Switzerland) · 2022Article
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3 authors.
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Abstract
No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes.
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