基于可解释多任务学习模型揭示糖尿病联合并发症的关键特征及预测建模OA

Complications of diabetes mellitus are important factors in patient mortality,and revealing their key features can effectively help physicians develop targeted intervention strategies to reduce the risk of death in comorbid conditions.However,most previous studies have focused on identifying risk factors for a single complication of diabetes,ignoring potential associations between complications.Therefore,based on the Diabetes Complications Early Warning Dataset provided by the National Population Health Sciences Data Centre,we used Pearson's correlation coefficient and the chi-square test to screen out significantly associated diabetic complications and incorporated them into a multi-task learning model for joint modeling.Then the importance of each feature was assessed using SHAP(SHapley Additive exPlanations),and 11 features with SHAP values higher than the 75%quartile were screened as significant risk factors for diabetes co-morbidities.A predictive model for diabetes-related complications was constructed using random forest,logistic regression,gradient boosting,extreme gradient boosting,adaptive boosting,and categorical feature gradient boosting.Input variables comprised features with SHAP values exceeding the 25th percentile.Optimal parameter combinations were selected via grid search,with model predictive performance evaluated using metrics including accuracy,precision,F1-score,and AUC.Results indicated that features selected through the interpretable multi-task learning model constituted key predictors,with all six predictive models achieving AUC values approaching 0.90.Finally,LIME(Local Interpretable Model-Agnostic Explanations)was introduced to interpret the model outcomes,thereby further validating the effectiveness and reliability of the constructed interpretable multi-task learning model for screening key features.The interpretable multi-task learning model comprehensively accounts for the underlying relationships between complications,enabling the precise identification of key risk factors for concurrent diabetic complications.This assists clinicians in formulating targeted intervention strategies,thereby helping to reduce patient mortality attributable to complications.