Development of an "all-data-on-hand" deep learning model to predict hospitalization for diabetic ketoacidosis (DKA) in youth with type 1 diabetes (T1D).

The proposed LSTM model for predicting 180-day DKA-related hospitalization is valid in the present sample. Future work should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (e.g., racially and/or socioeconomically diverse cohorts). Rank-ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.