Comparing the predictive performance of diabetes complications using administrative health data and clinical data.

ABSTRACT: When predicting adverse complications due to Type 2 Diabetes, often two different approaches are taken: predictions based on clinical data or those using administrative health data. No studies have assessed whether these two approaches reach comparable predictions. This study compares the predictive performance of these two data sources and examines the algorithmic fairness of the developed models. We developed XGBoost models to predict the two-year risk of nephropathy, tissue infection, and cardiovascular events in Type 2 Diabetes patients. The models using only clinical data achieved an average AUC of 0.78, while the models using administrative health data alone achieved 0.77. A hybrid model combining both data types resulted in an average AUC of 0.80, across complications. The models sh

SUBMITTER: Aagaard A 

PROVIDER: S-EPMC12475055 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature