Conclusions
Medical disputes are a prevalent issue among medical workers. Among the models compared, GBDT demonstrated a promising predictive performance. This web-based tool serves as a screening tool to identify medical workers who are at a higher risk of experiencing medical disputes. Preventive strategies should be emphasized, particularly in the high-risk group. Enhancing the knowledge and awareness of the medical law among medical workers can be effective in preventing medical disputes.The authors would like to express gratitude to the Beijing Health Commission and the Hunan Provincial Health Commission for their support in data collection and to all the medical workers who participated in this survey. This study was supported by the National Social Science Foundation of China (number 21STA052) and the Project of Hunan Provincial Health Commission.Authors' Contributions: MY and Yanlin Cao were responsible for conception and design. MY performed statistical data collection and analysis. YZ and CS helped with data collection. YG assisted in statistical analysis. MY drafted the manuscript. Yuebin Cao, L Wang, YG, XZ, JW, WC, and L Wei reviewed the manuscript. MY and Yanlin Cao verified the data. All the authors critically revised the manuscript and approved the final version before submission.Conflicts of Interest: None declared.Methods, results, and references of the study.A comprehensive analysis, comparing the characteristics of participants with and without medical disputes in both the Hunan and Beijing databases, the multivariate analysis to identify the key characteristics that predict medical disputes in the training group, the machine learning-based approaches and hyperparameters, the prediction performance of these machine learning approaches in the external validation group, and the risk stratification, validated internally and externally, to effectively identify individuals at different risk levels for medical disputes.The area under the receiver operating curve, violin plots for the discrimination slope, calibration curves, decision curve analysis, the heatmap of Shapley additive explanations (SHAP) values in the internal validation set, analysis of feature importance based on SHAP summary plot in the internal and external validation set, and the web-based application.