文章基本信息

标题：Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models
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作者：Norio Yamamoto ; Shintaro Sukegawa ; Takashi Watari 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2022
卷号：10
期号：5
DOI：10.3390/healthcare10050892
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：enmedical malpractice claimslitigationdiagnostic errormedical errorsystem errormachine learningprediction model