Introduction
REGRESSION
Medical disputes often arise from differences in the perceptions of treatment outcomes between patients and physicians [The identification of predictors for medical disputes is crucial for guiding the implementation of preventive strategies. Previous studies have highlighted that institutional failures in the legal framework, inappropriate internal incentives, patient-physician mistrust, heavy physician workloads, and medical malpractice are risk factors [However, studies investigating the relationship between medical disputes and hospital legal constructions have not fully addressed the role of medical workers. In addition, the development of a prediction model for medical disputes would be beneficial for conducting personalized interventions, as the risk probability of experiencing medical disputes can be accurately evaluated. Nonetheless, no prediction model has been developed for physicians and nurses to evaluate the risk of experiencing medical disputes. Currently, machine learning (ML) is widely used to establish accurate prediction models in medicine [To address these gaps, this study aimed to propose an accurate prediction model based on hospital legal construction to stratify medical staff according to their risk of experiencing medical disputes in the upcoming year. Logistic regression (LR) and 5 ML techniques—decision tree (DT), random forest (RF), support vector machine (SVM), gradient boosting DT (GBDT), and deep neural network (DNN)—were used to train and optimize the models. We hypothesized that these models would be able to identify medical staff who are most likely to experience medical disputes, thereby enabling the early implementation of preventive measures.