Predictive Model Libraries for Hypoglycemia Using Patient Data
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Solution Overview
Problem
Existing methods fail to accurately predict future medical episodes requiring intervention, particularly hypoglycemic events in diabetes patients, due to the lack of comprehensive data analysis and integration of patient-reported information.
Innovation Solution
A system and method for creating a library of predictive models using machine learning algorithms to analyze patient data, including self-reported and sensor data, to identify potential medical interventions by selecting appropriate models based on metadata extraction and applying them to generate timely notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If continuous glucose monitoring devices are used to immediately respond to hypoglycemic episodes, then response time is improved, but the ability to accurately predict future hypoglycemic episodes based on patient-reported data is worsened
Solution Approach 1:
The system segments the prediction task into multiple specialized models, each trained on specific data types (CGM data, SMBG data, or both). This allows the system to select the most appropriate model based on data availability, improving prediction accuracy for future episodes while maintaining rapid response capabilities through the alert generation component.
Solution Approach 2:
The system changes the parameter of data source utilization by offering multiple model variants that accept different input data types. By selecting the appropriate model based on whether CGM, SMBG, or combined data is available, the system optimizes prediction accuracy for each specific scenario while maintaining fast response times through automated alert generation.
2Measurement precision
If a library of multiple predictive models is created to handle different data types, then prediction accuracy is improved, but system complexity is worsened
Solution Approach 1:
The system implements dynamic model selection based on the type of input data received. Rather than using a single complex model or requiring manual configuration, the system automatically selects the appropriate pre-trained model (CGM-based, SMBG-based, or combined) based on data availability, simplifying operation while maintaining high prediction accuracy across different scenarios.
Solution Approach 2:
The system creates multiple specialized model copies, each optimized for specific data types (CGM data, SMBG data, or both). This allows the system to maintain simple, purpose-built models for each data type rather than attempting to create one universally complex model, improving accuracy while keeping individual model complexity manageable.
3Quantity of substance
If patient-reported self-monitored blood glucose data is used for prediction, then data availability is improved, but measurement precision is worsened compared to continuous glucose monitoring
Solution Approach 1:
The system segments its prediction capability into different model types based on data source quality and availability. By providing separate models for CGM data, SMBG data, and combined data, the system can optimally utilize patient-reported SMBG data when CGM data is unavailable, improving overall data availability while maintaining appropriate prediction standards for each data type.
Solution Approach 2:
The system changes the parameter of data source utilization by offering multiple model variants that accept different input data types. This allows the system to adapt to varying data availability scenarios, using patient-reported SMBG data effectively when needed while maintaining higher precision when CGM data is available, thus balancing availability and precision dynamically.
Data Source
AI summary
Computer implemented methods are disclosed. The methods may include receiving historical data comprising at least one of provider data and patient data, and processing, using a processor, the historical data to identify one or more patterns. The method also may include generating one or more decision models from the historical data and the decision patterns, and providing one or more recommendations based on the one or more decision models.