# Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes

> **NIH NIH R21** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $162,600

## Abstract

Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and
Prevent Nocturnal Hypoglycemia in Type 1 Diabetes
Project Summary
Hypoglycemia (glucose < 70 mg/dL) remains the limiting factor for achieving optimal glycemic control in type 1
diabetes (T1D), with nocturnal hypoglycemia being particularly dangerous. Nocturnal hypoglycemia may result
in physical injury, poor sleep quality, fear of hypoglycemia, and hypoglycemia unawareness. Severe episodes
can cause seizures and unconsciousness requiring emergency care, and even death (dead in bed syndrome).
While automated insulin delivery (AID) systems have shown benefits in glucose control during the night,
nighttime hypoglycemia still occurs. Moreover, many people with T1D manage their glucose with continuous
subcutaneous infusion pump (CSII) therapy or multiple daily insulin injections (MDI) therapy. Data updated
between 2013 and 2014 from 16,061 individuals with T1D participating in the T1D Exchange clinic registry
showed that approximately 40% participants managed their glucose with MDI. In this project, we propose to
develop and evaluate a personalized decision support tool that collects and analyzes glucose measurements,
insulin, meals, and physical activity data to predict at bedtime the likelihood of overnight hypoglycemia and
recommend a proactive carbohydrate intervention to substantially reduce nocturnal hypoglycemia. In the
engineering development phase of the project, we will use unique datasets of time-matched glucose
management data (i.e., continuous glucose measurements, insulin, meals, and exercise) from pump, closed-
loop and MDI users to extract information about the major contributors to nocturnal hypoglycemia risk and train
a population-based prediction model that will be personalized over time to better capture inter-subject
variability. We will design a bedtime intervention consisting of a bedtime smart snack with variable nutrient
content that can prevent nighttime hypoglycemia. Snacks will vary by macronutrient content and size to
optimize time to peak post-prandial glycemia that will match the timing to predicted episode of hypoglycemia.
We will conduct a randomized cross-over study to evaluate our smartphone-based decision support tool on a
cohort of 20 people with T1D who are MDI users and are at higher risk of experiencing hypoglycemia.
Participants will be randomly assigned to either first use CGM only (control period) followed by a smartphone-
based decision support tool + nocturnal hypoglycemia intervention (intervention period), or vice-versa. The
control and intervention periods will have a duration of three weeks each. We will measure the effect of the
intervention by comparing the percent time in nocturnal hypoglycemia during the control period vs. the
intervention period. We will also retrospectively measure the accuracy of the prediction model in predicting
nocturnal hypoglycemia using data from the control period. We expect that ...

## Key facts

- **NIH application ID:** 10373516
- **Project number:** 1R21DK128582-01A1
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Clara Marcela Mosquera-Lopez
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $162,600
- **Award type:** 1
- **Project period:** 2021-09-21 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10373516

## Citation

> US National Institutes of Health, RePORTER application 10373516, Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes (1R21DK128582-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10373516. Licensed CC0.

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