Demographic Feature Space Partitioner for Targeted Therapeutics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current drug development clinical trials face challenges in evaluating the effectiveness and safety of drugs across different demographic groups due to high variability in drug response and adverse reactions, making it impractical to assess all possible grouping possibilities, leading to ineffective therapies for conditions like hypertension.
Innovation Solution
An automated method optimally partitions the demographic feature space into distinct groups using a multi-objective optimization process that minimizes inter-group distance and maximizes intra-group commonality through a weighted sum of omics, physiological, EMR, and contextual cost functions, enabling the development of targeted therapeutics for specific demographic sub-populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible grouping possibilities are evaluated in the feature space, then the accuracy of identifying distinct demographic sub-populations is improved, but the computational cost and time required become prohibitively expensive
Solution Approach 1:
The patent segments the continuous feature space into discrete demographic groups by identifying optimal partitioning parameters that divide the space into distinct clusters. This segmentation approach allows the system to evaluate representative groups rather than all possible combinations, significantly reducing computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent transforms the complex multi-dimensional feature space evaluation problem into a parameter optimization problem. By changing the approach from evaluating all groupings to optimizing partitioning parameters (such as threshold values and cluster centers), the system achieves accurate sub-population identification with reduced computational burden.
2Reliability
If demographic groups are partitioned to maximize intra-group commonality and minimize inter-group differences, then the effectiveness of targeted therapeutics is improved, but the complexity of the partitioning algorithm increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the objective function and constraints for the partitioning algorithm before execution. The cost function that measures intra-group commonality and inter-group differences is established in advance, allowing the algorithm to efficiently search for optimal partitions without complex real-time calculations.
Solution Approach 2:
The patent replaces complex mechanical evaluation of all possible groupings with a mathematical optimization approach. By substituting the brute-force evaluation mechanism with an optimization algorithm that minimizes a well-defined cost function, the system achieves reliable therapeutic targeting with manageable algorithmic complexity.
Data Source
AI summary
Systems and methods for demographic grouping are disclosed. In certain embodiments, the technology involves receiving a dataset comprised of one or more of omics, physiological, EMR and contextual data and minimizing a weighted sum multi-objective function at an optimizer through a multi-objective optimization process. A plurality of constraints, initial conditions and hyperparameters are applied to the objective function and optimization process to generate potential sub-population clusters. Then the potential sub-population clusters are compared through statistical and functional evaluation of differentially expressed genes and gene ontology resulting in the optimal solution for the targeted phenotype as the output.