Hierarchical Neurostimulation Setting Optimization Using Patient Clusters
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Solution Overview
Problem
Current neurostimulation systems face challenges in efficiently optimizing settings for individual patients, leading to prolonged optimization processes and potential barriers to achieving therapeutic benefits, especially for patients with limited trial periods or psychological barriers.
Innovation Solution
A method is introduced that involves receiving population-based optimized neurostimulation settings, extracting clusters of settings, determining test settings that discriminate between clusters, presenting these to users, and iteratively updating user-based settings based on preference data to map the user to a specific cluster, thereby optimizing neurostimulation settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional individual optimization methods are used for neurostimulation settings, then personalized treatment accuracy is improved, but optimization time is excessively prolonged
Solution Approach 1:
The system performs preliminary optimization by analyzing population data from multiple patients to pre-identify clusters of neurostimulation settings that are likely to be effective. This preliminary action creates a narrowed search space for individual patient optimization, avoiding the need to test all possible settings combinations from scratch for each patient.
Solution Approach 2:
The patent segments the population of patients into distinct clusters based on their responses to neurostimulation settings. By dividing the patient population into segments with similar characteristics, the system can apply cluster-specific optimization strategies, reducing the overall optimization burden for each individual patient while maintaining personalized treatment accuracy.
2Measurement precision
If comprehensive testing of multiple neurostimulation settings is performed, then identification of effective settings is improved, but patient burden and psychological barriers increase
Solution Approach 1:
The system implements a feedback mechanism where patient responses to initial test settings are analyzed to update and refine the clusters. This feedback loop allows the system to adapt to individual patient preferences and physiological responses, progressively narrowing down to the most effective settings with fewer tests while maintaining high identification accuracy.
Solution Approach 2:
Instead of requiring exhaustive testing of all possible settings, the system performs partial action by testing only a representative subset of settings within each cluster. This approach identifies effective settings with sufficient accuracy while significantly reducing the number of tests required compared to comprehensive evaluation of all possible parameters.
3Productivity
If population-based clustering is used to accelerate optimization, then optimization speed is improved, but individual patient specificity may be reduced
Solution Approach 1:
The clustering system is dynamic rather than static. Clusters are continuously updated and refined based on individual patient feedback and response data. This dynamic adaptation allows the system to maintain population-level optimization speed while progressively improving individual patient specificity as more data becomes available during the optimization process.
Solution Approach 2:
The system applies local quality by tailoring the optimization approach to each patient's specific characteristics within their assigned cluster. While clusters provide a population-level framework for efficient optimization, the system locally adapts to individual patient responses, ensuring that the final settings are specifically optimized for each patient's unique needs and physiological characteristics.
Data Source
AI summary
User-specific neurostimulation settings are efficiently determined and optimized based on an optimized set of population-based neurostimulation settings. The population data are clustered and a set of test settings for a new user are selected as settings that efficiently discriminate between the clusters. User preference of the test settings are used to map the user to a particular cluster of settings, which can be used to determine user-specific neurostimulation settings. The user-based settings can be iteratively updated and/or optimized using information from the population data, such as by using average preference score surfaces in the population data to identify and/or filter new test settings for the user.