• Long Vu, Sarah Kefayati, Tsuyoshi Idé, Venkata Pavuluri, Gretchen Jackson, Lisa Latts, Yuxiang Zhong, Pratik Agrawal, and Yuan-Chi Chang.
• IBM Research AI, Yorktown Heights, NY, USA.
• AMIA Annu Symp Proc. 2019 Jan 1; 2019: 874-882.

AbstractNocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been limited to a short prediction horizon (~ 30 minutes). To this end, a nocturnal hypoglycemia prediction model with a 6-hour horizon (midnight-6 am) was developed using a random forest machine- learning model based on data from 10,000 users with more than 1 million nights of CGM data. The model demonstrated an overall nighttime hypoglycemia prediction performance of ROC AUC = 0.84, with AUC = 0.90 for early night (midnight-3 am) and AUC = 0.75 for late night (prediction at midnight, looking at 3-6 am window). While instabilities and the absence of late-night blood glucose patterns introduce predictability challenges, this 6-hour horizon model demonstrates good performance in predicting nocturnal hypoglycemia. Additional study and specific patient-specific features will provide refinements that further ensure safe overnight management of glycemia.©2019 AMIA - All rights reserved.

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