Interpretable and multimodal fusion methodology to predict severe hypoglycemia in adults with type 1 diabetes

Abstract

Type 1 diabetes (T1D) causes insulin deficiency and exogenous therapy is required for maintaining targeted glucose levels. Hypoglycemia is the most frequent side effect of insulin, being severe hypoglycemia (SH) one of the most critical hazards with a range of life-threatening consequences. Artificial intelligence (AI) and multimodal fusion have boosted predictive performance in different domains. This study aims to evaluate the effectiveness of early fusion (EF) and late fusion (LF) approaches for predicting SH, to create a methodology capable of achieving robust results in datasets with a low number of samples for predicting SH and to characterize the risk factors involved in the SH onset using explainable AI (XAI). Data from a case-control study comprising adults over 60 years with T1D and with diabetes duration of 20 years were used and three types of modalities were considered: (1) continuous glucose monitoring data (time series); (2) clinical codes (text); and (3) surveys related to fear, unawareness, depression, and cognitive tests (tabular data). The results revealed that EF outperformed models trained with single-modality data by 5.8%, with an area under the receiver operating characteristic curve of 0.779. XAI techniques helped to discover that features related to fear and unawareness are mainly associated with SH. Our study introduced an interpretable and multimodal methodology capable of predicting the occurrence of SH in adults with T1D in the next year. Our interpretable methodology contributes to predicting SH and identifying related key factors, thus preventing SH complications and improving patient's quality of life.