Adaptive Predictive Modeling for Continuous Sensor Data
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Solution Overview
Problem
Existing predictive modeling techniques for complex domains like diabetes diagnostics are reactive, generic, and fail to provide personalized, real-time insights due to their reliance on population-based protocols, lacking adaptability and precision in interpreting continuous sensor measurements.
Innovation Solution
The development of predictive modeling techniques that identify change points and data spikes in continuous sensor measurements to generate personalized and adaptive predictive classifications, enabling tailored action outputs based on historical observations and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If population-based sequential protocols are used for predictive modeling, then the model can be simpler and faster to implement, but the predictive insights become generalized rather than personalized and fail to adapt to real-time information
Solution Approach 1:
The patent implements dynamic predictive modeling by continuously updating models with new real-time sensor data and historical observations. The system transitions from static population-based protocols to dynamic individual-specific models that adapt as new data becomes available, enabling personalized predictions that evolve over time based on continuous learning from sensor measurements and outcomes.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing historical sensor data and outcomes before generating predictive classifications. This allows the model to learn from past patterns and prepare individual-specific predictive frameworks in advance, enabling rapid and accurate real-time predictions without requiring complex computations at the moment of prediction.
2Productivity
If trial and error protocols are used to determine optimal actions, then the approach is simpler to implement, but the process becomes slow and expensive without personalized insights
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring sensor measurements and treatment outcomes, then using this information to refine and update predictive models. The system learns from actual outcomes and adjusts future predictions accordingly, creating a closed-loop system that improves precision over time while accelerating treatment selection through data-driven insights rather than trial and error.
Solution Approach 2:
The system replaces the mechanical trial-and-error approach with an automated data-driven predictive modeling system. Instead of systematically testing multiple treatments and observing outcomes, the model directly predicts optimal actions by processing sensor data and historical information, significantly increasing speed while improving precision through continuous learning and adaptation.
3Measurement precision
If continuous sensor measurements are processed to generate personalized predictive classifications, then predictive insights become tailored to individuals, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the continuous sensor measurement stream into discrete time intervals and identifying specific change points where significant transitions occur. This segments the complex continuous data into manageable segments that can be processed individually, reducing computational complexity while maintaining precision through focused analysis of critical transition points rather than processing all data uniformly.
Solution Approach 2:
The system implements local quality by applying different processing techniques to different segments of the data based on their characteristics. Critical segments (change points) receive enhanced processing for high precision predictions, while stable segments are processed more lightly. This localized approach maintains high measurement precision for critical predictions while managing overall processing complexity through selective computation.
Data Source
AI summary
Various embodiments of the present disclosure provide predictive modeling techniques for generating predictive classifications from a plurality of continuous sensor measurements. The techniques may include identifying change points from sensor measurements for an input data object, determining data spikes from the sensor measurements based on the change points, and generating a predictive classification for the input data object based on the data spikes. The predictive classification may correspond to an evaluation time period with one or more sub-time periods. The techniques may include determining a sub-time period classification for each of the sub-time periods of the evaluation time period. The predictive classification may be derived from the sub-time period classifications. Using the predictive classification, an action output may be generated and provided for the input data object.