Adaptive Group Testing for Lifestyle Intervention Potency
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Solution Overview
Problem
Current methods for identifying effective lifestyle interventions are inefficient due to the large combinatorial search space, requiring extensive trial-and-error and relying heavily on biomarkers that are difficult to acquire and interpret.
Innovation Solution
The Algorithmic Lifestyle Optimization (ALO) server employs a group testing algorithm to rapidly identify effective lifestyle interventions by using an adaptive strategy that minimizes the number of rounds required to determine the potency of each intervention, even in the presence of noise, varying data sizes, and heterogeneity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trial-and-error approaches are used to evaluate lifestyle interventions, then individual response can be determined, but the time required increases significantly
Solution Approach 1:
The patent segments the large combinatorial search space of lifestyle interventions into smaller, manageable groups. By dividing interventions into categories and evaluating them in structured phases rather than through random trial-and-error, the system determines individual responses more efficiently while reducing the overall time required for evaluation.
Solution Approach 2:
The patent implements preliminary actions by using biomarkers to predict individual responses before actual intervention trials. This preliminary screening allows the system to prioritize which interventions are most likely to be effective for each individual, reducing the number of trials needed and thereby reducing time while maintaining measurement precision.
2Productivity
If multiple lifestyle interventions are tested simultaneously, then the number of candidate interventions evaluated increases, but the complexity of the search space increases
Solution Approach 1:
The patent applies segmentation by organizing the complex search space into hierarchical groups and subsets. Instead of evaluating all possible intervention combinations simultaneously, the system divides interventions into manageable categories and evaluates them in structured phases, thereby increasing productivity while controlling complexity through systematic organization.
Solution Approach 2:
The patent implements dynamics by adapting the evaluation process based on intermediate results. The system dynamically adjusts which interventions are tested next based on individual responses and biomarker data, allowing efficient evaluation of multiple interventions while managing complexity through flexible, results-driven progression rather than rigid simultaneous testing.
3Measurement precision
If standard elimination diet methods are used, then food allergies and intolerances can be identified, but the process is time-consuming and inconvenient
Solution Approach 1:
The patent applies preliminary action by using biomarkers to predict individual responses to dietary interventions before the elimination diet process begins. This preliminary assessment allows for more targeted and efficient identification of food allergies and intolerances, reducing the time required while maintaining the precision needed for accurate diagnosis.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring biomarker changes and individual responses during the elimination diet process. This real-time feedback allows for dynamic adjustment of the diet protocol, enabling faster identification of problematic foods while maintaining measurement precision for accurate allergy and intolerance diagnosis.
Data Source
AI summary
Described herein is a system and method for determining potency of lifestyle interventions. For a set of lifestyle interventions (LIs), a potency probability is estimated for each LI. A catalog is generated to include data associated with the set of LIs. The set of LIs is partitioned into a plurality of disjoint sets of LIs based on the catalog and the estimated potency probabilities. For each disjoint set, a model is applied to determine a potency of each LI such that an optimum number of rounds are utilized in determining potency of each LI included in the set. The set of LIs and their computed potencies are rendered in a graphical user interface displayed on a user's device.