Diabetes Prediction Model Using Population Segmentation
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Solution Overview
Problem
A significant portion of diabetes cases in the United States remain undiagnosed, leading to increased health risks and costs, as patients unaware of their condition do not receive timely treatment, and existing methods lack effective prediction and monitoring tools to identify those at risk of developing or progressing to severe diabetes.
Innovation Solution
A computerized system and method that uses predictive models to analyze population data from various sources, including insurance claims, lab results, and demographics, to identify segments at risk of developing diabetes or experiencing disease progression, allowing for proactive monitoring and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are implemented to identify high-risk individuals, then early detection and management of diabetes is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the population into different risk categories (low, moderate, high risk) based on predictive model outputs. This allows targeted interventions for high-risk individuals while maintaining system manageability. The segmentation principle resolves the complexity contradiction by organizing complex data into actionable risk stratifications.
Solution Approach 2:
The patent introduces an intermediary data processing layer that transforms raw data from multiple sources into standardized risk assessments. This intermediary layer simplifies the overall system architecture by abstracting complex data processing details while maintaining high detection accuracy through structured data transformation and risk calculation frameworks.
2Measurement precision
If comprehensive data from multiple sources is analyzed, then prediction accuracy is improved, but data processing time and resource requirements increase
Solution Approach 1:
The system performs preliminary data cleaning, standardization, and feature selection before running predictive models. This preliminary action reduces the computational burden on subsequent processing steps and accelerates analysis time while maintaining precision by pre-processing data to retain only relevant features for risk prediction.
Solution Approach 2:
The patent extracts and focuses on the most predictive features from comprehensive data sources, filtering out redundant or less informative data elements. This extraction principle maintains high prediction precision by concentrating on key risk indicators while significantly reducing data processing time and resource requirements compared to analyzing all available data equally.
3Reliability
If proactive monitoring and intervention are implemented, then disease progression is reduced, but healthcare costs increase
Solution Approach 1:
The system applies local quality by providing targeted monitoring and intervention resources specifically to high-risk individuals identified by the predictive model, rather than uniformly distributing resources across the entire population. This resolves the cost contradiction by concentrating healthcare energy where it is most needed and most likely to prevent progression, improving cost-effectiveness.
Solution Approach 2:
The patent implements preliminary action by identifying at-risk individuals before disease progression occurs, enabling preventive interventions that are less costly than treating advanced disease. By acting in advance for high-risk patients, the system reduces long-term healthcare costs while maintaining high disease management effectiveness through early-stage prevention strategies.
Data Source
AI summary
The disclosed computerized system and method facilitates predicting the onset of diabetes or symptom progression in those patients already suffering from the disease. The computerized system and method applies steps to segment the population by predefined member characteristics. Once segmented, the computerized system and method applies a plurality of prediction models to the segmented population data to provide a ranking of members of the population that indicates the likelihood of onset or progression of diabetes for each member.