On-Demand Overnight Hypoglycemia Risk Detection
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Solution Overview
Problem
Existing methods for predicting hypoglycemia over extended time horizons, such as overnight periods, are unreliable due to uncertainties from unknown factors like eating, activity, and stress, leading to frequent false positive alerts and reduced accuracy.
Innovation Solution
A risk detection model that predicts overnight hypoglycemic risk by training on factors less likely to disturb glucose levels at night, providing notifications and recommendations for mitigating actions, and utilizing a CGM application to display visual elements for user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time systems continuously predict glucose levels at future elapsed times, then hypoglycemia detection capability is provided, but prediction accuracy deteriorates over extended time horizons due to unknown factors
Solution Approach 1:
The patent segments the prediction task into two distinct components: (1) a risk detection model that predicts the probability of hypoglycemia occurring within a future time window, and (2) a separate glucose level prediction system for immediate future times. This segmentation allows the risk model to focus on detecting hypoglycemic events without being constrained by the accumulating uncertainty of extended glucose level predictions, thereby improving reliability while maintaining precision for immediate predictions.
Solution Approach 2:
The system performs preliminary risk assessment by evaluating multiple potential future scenarios and calculating the probability of hypoglycemia before the actual event occurs. The risk detection model uses historical data, physiological parameters, and environmental factors to compute overnight hypo risk in advance, enabling proactive patient intervention before glucose levels actually drop to hypoglycemic ranges.
2Adaptability or versatility
If prediction time horizon is extended to cover overnight periods, then patient vulnerability coverage is improved, but false positive alerts increase due to unknown factors
Solution Approach 1:
The risk detection model dynamically adjusts its prediction approach based on the specific time horizon being evaluated. For overnight periods, the model incorporates nighttime-specific physiological patterns, insulin pharmacokinetics, and reduced metabolic activity factors. The system adapts its uncertainty modeling to account for the unique characteristics of extended time horizons, dynamically weighting different input factors based on their relevance to overnight hypo risk rather than applying static prediction parameters.
Solution Approach 2:
The patent changes key prediction parameters when evaluating extended time horizons versus immediate future predictions. The risk detection model uses different probability thresholds, time-weighting factors, and uncertainty margins compared to real-time glucose prediction. By adjusting these parameters based on the prediction horizon, the system maintains alert accuracy across varying time scales while providing comprehensive coverage for overnight periods.
3Reliability
If multiple unknown factors are considered in prediction models, then comprehensive risk assessment is achieved, but model complexity increases
Solution Approach 1:
The patent extracts and isolates the specific factors most critical for overnight hypoglycemia risk from the full set of potential glucose-affecting variables. Rather than incorporating all possible unknown factors into a single complex model, the system identifies and extracts key nighttime-relevant parameters such as basal insulin rates, nighttime carbohydrate intake, sleep patterns, and morning cortisol levels. This extraction approach maintains comprehensive risk assessment for overnight periods while reducing model complexity by focusing on the most influential factors.
Solution Approach 2:
The risk detection model serves as an intermediary layer between raw sensor data and clinical decision-making. It aggregates multiple input factors including glucose sensor readings, insulin pump data, activity monitors, and user-reported information, processing them through a unified probability calculation framework. This intermediary model simplifies the complexity by providing a single integrated risk probability output rather than requiring clinicians to manually evaluate multiple separate factors, thereby achieving comprehensive assessment while maintaining model tractability.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating determining overnight hypoglycemia risk by estimating the likelihood of a hypoglycemic event occurring over a specified period of time, namely overnight. The disclosure describes utilizing two key aspects: factors that can disturb glucose levels are much less likely to occur overnight, and bedtime is a convenient and beneficial time for the patient to check for and mitigate their risk of hypoglycemia overnight.