Stimulation Response Profiles Prediction Generator
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Solution Overview
Problem
Current medical devices for electrical stimulation therapy face challenges in predicting long-term efficacy, often resulting in false negatives due to short-term response fluctuations and gaps in data collection, which can lead to inappropriate therapy adjustments.
Innovation Solution
A system and method that utilize a prediction generator to determine long-term efficacy indicators by correlating efficacy data with historical response profiles, identifying specific efficacy times for data collection and analysis, and generating predictions of long-term therapy efficacy based on these indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If short-term response data is used to evaluate therapy efficacy, then immediate therapy adjustment is possible, but false negatives occur due to short-term response fluctuations
Solution Approach 1:
The system pre-identifies efficacy times based on historical response profiles before evaluating patient response. By determining when efficacy data should be collected in advance (rather than continuously), the system avoids false negatives from short-term fluctuations while maintaining timely evaluation capability.
Solution Approach 2:
The system creates a model (copy) of the expected response profile based on historical data from multiple patients. This predicted response profile serves as a template to compare against actual patient responses at specific efficacy times, improving evaluation reliability without requiring continuous monitoring.
2Loss of information
If continuous data collection is performed to capture all response variations, then complete response profile is obtained, but data gaps remain due to practical collection constraints
Solution Approach 1:
The system extracts only the most critical data points at pre-identified efficacy times from the continuous response profile. By taking out only these essential measurements (rather than collecting all possible data), the system maintains evaluation accuracy while significantly reducing data collection burden and eliminating gaps caused by practical constraints.
Solution Approach 2:
The continuous response evaluation period is segmented into discrete efficacy times based on historical patterns. Instead of treating response evaluation as a continuous process, the system divides it into specific time points where measurements are most informative, making data collection more feasible while preserving critical information.
3Productivity
If therapy parameters are adjusted frequently based on short-term responses, then rapid optimization is achieved, but long-term efficacy is compromised due to premature adjustments
Solution Approach 1:
The system performs preliminary identification of efficacy times before making therapy adjustments. By determining in advance when response evaluation should occur based on historical profiles, the system avoids premature adjustments while maintaining efficient optimization timing, thus preserving long-term efficacy.
Solution Approach 2:
The system uses feedback from comparing actual patient responses at efficacy times against the predicted response profile to guide therapy adjustments. This feedback mechanism ensures adjustments are made only when meaningful response patterns are detected, balancing optimization efficiency with long-term efficacy preservation.
Data Source
AI summary
Techniques for providing therapy to a patient via electrical stimulation are described. The techniques include, for example, determining, relative to a start time of providing the electrical stimulation, one or more efficacy times that correspond to an efficacy indicator, determining, according to the efficacy times, efficacy data items for the patient, comparing the efficacy data items with the efficacy indicator, and generating, based on the comparison, a prediction of an expected response to the therapy manifesting in the patient at a prospective time.