Explainable Prediction of Long-Term Glycated Hemoglobin Response Change in Finnish Patients with Type 2 Diabetes Following Drug Initiation Using Evidence-Based Machine Learning Approaches.

PURPOSE

This study applied machine learning (ML) and explainable artificial intelligence (XAI) to predict changes in HbA1c levels, a critical biomarker for monitoring glycemic control, within 12 months of initiating a new antidiabetic drug in patients diagnosed with type 2 diabetes. It also aimed to identify the predictors associated with these changes.

PATIENTS AND METHODS

Electronic health records (EHR) from 10,139 type 2 diabetes patients in North Karelia, Finland, were used to train models integrating randomized controlled trial (RCT)-derived HbA1c change values as predictors, creating offset models that integrate RCT insights with real-world data. Various ML models-including linear regression (LR), multi-layer perceptron (MLP), ridge regression (RR), random forest (RF), and XGBoost (XGB)-were evaluated using and RMSE metrics. Baseline models used data at or before drug initiation, while follow-up models included the first post-drug HbA1c measurement, improving performance by incorporating dynamic patient data. Model performance was also compared to expected HbA1c changes from clinical trials.

RESULTS

Results showed that ML models outperform RCT model, while LR, MLP, and RR models had comparable performance, RF and XGB models exhibited overfitting. The follow-up MLP model outperformed the baseline MLP model, with higher scores (0.74, 0.65) and lower RMSE values (6.94, 7.62), compared to the baseline model (R²: 0.52, 0.54; RMSE: 9.27, 9.50). Key predictors of HbA1c change included baseline and post-drug initiation HbA1c values, fasting plasma glucose, and HDL cholesterol.

CONCLUSION

Using EHR and ML models allows for the development of more realistic and individualized predictions of HbA1c changes, accounting for more diverse patient populations and their heterogeneous nature, offering more tailored and effective treatment strategies for managing T2D. The use of XAI provided insights into the influence of specific predictors, enhancing model interpretability and clinical relevance. Future research will explore treatment selection models.