Genetic Algorithm Cohort Selection for Agile Clinical Trials
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Solution Overview
Problem
Conventional methods for assigning entities to cohorts in agile processes, such as clinical studies, rely on static criteria, leading to suboptimal cohort assignments and inefficient treatment/medicine efficacy determination.
Innovation Solution
A method and system utilizing genetic algorithms to dynamically create and evaluate cohorts by encoding environmental, epigenetic, physiological, and cognitive traits into a DNA strand, allowing for iterative cohort selection and optimization, including crossover and mutation techniques to improve cohort matching and treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static criteria are used to assign entities to cohorts, then the assignment process is simple and quick, but the cohort assignments are suboptimal and treatment efficacy determination is inefficient
Solution Approach 1:
The patent applies dynamic optimization by using genetic algorithms that iteratively evolve cohort assignments. Instead of static criteria, the system dynamically adjusts entity assignments to cohorts through multiple generations of selection, crossover, and mutation operations, improving assignment quality while managing complexity through automated evolutionary processes
Solution Approach 2:
The system changes parameters by encoding entity attributes and cohort characteristics into configurable weightings and fitness functions. The genetic algorithm modifies these parameters across generations, adjusting the importance of different attributes and optimizing the overall cohort assignment quality based on treatment efficacy goals
2Reliability
If genetic algorithms are used to dynamically create cohorts, then optimal groupings and treatment efficacy are improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining the fitness function, attribute weightings, and selection criteria before the genetic algorithm execution. This preparation phase allows the algorithm to focus computational resources on optimization rather than parameter definition, reducing overall processing time while maintaining efficacy
Solution Approach 2:
The patent applies partial action by implementing early stopping criteria and iterative refinement where the genetic algorithm runs for a predetermined number of generations or until convergence is achieved. This prevents excessive computation while still obtaining near-optimal cohort assignments that satisfy treatment efficacy requirements
3Loss of information
If static criteria are used for cohort assignment, then the process is easy to implement, but non-obvious relationships and optimal groupings cannot be identified
Solution Approach 1:
The system implements feedback mechanisms where the fitness function evaluates cohort assignments based on treatment efficacy and feeds this information back into the genetic algorithm. This feedback loop allows the algorithm to learn from previous generations, identify non-obvious relationships between entity attributes, and progressively improve cohort groupings while managing complexity through structured evaluation criteria
Data Source
AI summary
Methods and systems for cohort selection for agile processes are disclosed. A method includes: assigning, by a computing device, each of a plurality of entities to one of a plurality of groups; and for each of the plurality of groups: for each of the entities in the group, determining, by the computing device, information about the entity and encoding the determined information about the entity using a genetic algorithm; determining a ranking, by the computing device, of each of the entities in the group based on the encoded information about each of entity in the group; crossing over, by the computing device, portions of the encoded information of pairs of entities occupying adjacent positions in the ranking; and measuring, by the computing device, fitness of each of the entities in the group.