An Algorithm for Retrospectively Detecting Missing Meal Records in Type 1 Diabetes Datasets

Data-driven technologies are becoming widespread in Type 1 Diabetes (T1D) research to deliver personalized care to patients. Missing or erroneous meal records represent a major factor affecting the quality of T1D data and, therefore, the development of these tools. This work proposes an algorithm based on logistic regression to retrospectively detect meals in T1D data. The algorithm is developed using real data from supervised clinical trials. During testing, absolute detection delay was 15 min on average and recall was > 0.80 - results that remained consistent despite of the proportion of missed and erroneous meals. False positive detection increased from 0.33 events/day (when 33% of meals were missing) to 0.66 (when all meals were missing).