Explainable Artificial Intelligence to predict clinical outcomes for adults with Type 1 diabetes

Abstract. Type 1 diabetes patients are prone to life-threatening conditions. Severe hypoglycemia and diabetic ketoacidosis are such conditions that often require urgent hospital care. Recently, artificial intelligence (AI) techniques have been used to improve the quality of diabetes care and management. These techniques provide a more comprehensive and better experience for patients and their loved ones. The objective of this study is to implement an AI-based explainable solution to predict possible severe hypoglycemia and diabetic ketoacidosis events in T1D patients within the next 12 months. The initial models in this study were built with baseline factors identified in prior research. However, baseline factors alone did not provide enough information, and the models were improved by introducing more features and separating the population by gender. The final predictive models highlighted some of the baseline factors in the original study when predicting the outcomes. Decision support systems based on machine learning models have become a viable way to enhance patient safety by locating and prioritizing high-risk patients. The final models were used to build a decision support system that facilitates precision medicine by prioritizing the high-risk patient group. Moreover, it helps to potentially reduce medical expenses through more efficient resource management

Explainable Artificial Intelligence to predict clinical outcomes in type 1 diabetes and relapsing-remitting multiple sclerosis adult patients

Abstract Artificial intelligence (AI) is increasingly being used to improve patient care and management. In this paper, we propose explainable AI (XAI) models for predicting severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) episodes in adults with type 1 diabetes (T1D) and relapses in adults with relapsing-remitting multiple sclerosis (RRMS). We follow a three-step process in this study: (1) develop baseline machine learning (ML) models, (2) improve the models using ReliefF feature selection technique, and develop sex-stratified models, (3) explain the models and their results using SHapley Additive exPlanations (SHAP). We built six ML models (XGBoost, LightGBM, CatBoost, AdaBoost, random forest, and linear regression) for all scenarios. Applying the ReliefF feature selection led to improved model performance in predicting all outcomes compared to the baseline models. Additionally, sex-stratified models further improved the prediction of SH episodes and relapses. The F1 scores for predicting SH episodes in male and female patients were 84.07% and 84.95%, respectively, and the DKA prediction model achieved an F1 score of 78.67%. The proposed relapse prediction models outperformed existing models with F1 scores of 84.55% (males) and 76.11% (females), and ROCs of 70.26% (males) and 69.05% (females). Our results highlight the importance of considering sex differences, socioeconomic factors, and physical and mental health in medical outcome prediction. Boosting ML algorithms were found to be effective in detecting SH and DKA in T1D patients and relapses in RRMS patients compared to conventional tree-based ML and statistical models