340-P: Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes Using Combined Supervised and Unsupervised Ensemble Learning

Abstract

The Aim: To assess the efficacy of combined supervised and unsupervised ensemble machine learning based on continuous glucose monitoring (CGM) data for short-term prediction of nocturnal hypoglycemia (NH) in hospitalized patients with type 1 diabetes (T1D). Materials and Methods: We analyzed CGM records with a minimal duration of 72 hours obtained from 406 adult patients admitted to tertiary referral hospital. An episode of NH was defined as ≥3 consequent glucose values of <3.9 mmol/L recorded between 0 and 6 am. Depending on predictor sequence length (30 min - 1 h) and horizon (H) of forecasting (5-30 min), we obtained 209-256 and near 4∙104 records with and without NH respectively. Twenty nine metrics, including mean, minimal and maximum glucose, range, gradient, linear, quadratic and cubic trend coefficients, dynamic time wrapping similarity-based indices, and a number of glucose variability indices, were evaluated. The ensemble clustering was used for extraction of representative samples. Random Forest ensemble model was applied for NH prediction. The obtained quality metrics were averaged over 100 random partitions of the dataset on training and testing subsets. Results: The quality metrics did not depend significantly on predictor sequence length; we used 30 min as a baseline. Depending on H, values of sensitivity and specificity varied from 98% and 97% (H=5 min) to 85% and 87% (H=30 min). The false alarm ratio values were very high (>90%) due to the unbalanced nature of data. Minimal glucose, low blood glucose index, and increment in the penultimate value of sequence were the most important predictors assessed with Random Forest algorithm. Conclusions: The results demonstrate the usefulness of ensemble machine learning models based on CGM time series data for prediction of NH events in hospitalized patients with T1D. Additional studies are needed for improving false alarm ratio metrics of forecasting quality. Disclosure V. Berikov: None. J. F. Semenova: None. V. Klimontov: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Sanofi, Research Support; Self; Boehringer Ingelheim International GmbH, Speaker’s Bureau; Self; AstraZeneca, Medtronic. Funding Russian Science Foundation (20-15-00057)