Probabilistic Graphical Models for Clinical Trial Design
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Researchers face challenges in determining the optimal values for fields such as inclusion criteria, exclusion criteria, and participant numbers when designing new clinical trials, due to variations in disease rarity, expected effect size, and budget constraints.
Innovation Solution
The use of probabilistic graphical models, specifically Neural Graphical Models, to capture non-linear dependencies in clinical trial data, allowing for counterfactual reasoning and providing probability distributions for clinical trial attributes based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If researchers manually determine field values based on related trials, then they can make design decisions, but the process is time-consuming and lacks precision
Solution Approach 1:
The patent replaces the manual mechanical process of researchers reviewing and comparing related trials with an automated probabilistic graphical model system. The PGM automatically learns from historical trial data and provides field value recommendations, eliminating the time-consuming manual analysis while improving precision through probabilistic reasoning and learned relationships in the data.
Solution Approach 2:
The probabilistic graphical model acts as an intermediary between historical trial data and new trial design decisions. It captures complex relationships and dependencies in the data, performing inference to suggest optimal field values that balance multiple factors like inclusion criteria, participant numbers, and expected outcomes without requiring direct manual analysis of each historical trial.
2Adaptability or versatility
If researchers consider multiple factors (disease rarity, effect size, budget), then the trial design becomes more comprehensive, but the complexity of determining optimal values increases
Solution Approach 1:
The patent segments the complex trial design problem into distinct fields and factors that can be independently analyzed. The probabilistic graphical model breaks down the determination of optimal values into separate inference tasks for each field (inclusion criteria, participant numbers, etc.), allowing comprehensive consideration of multiple factors while managing complexity through modular analysis.
Solution Approach 2:
The system handles multiple factors by changing and analyzing different parameters simultaneously within the probabilistic framework. The PGM learns relationships between various trial parameters (disease rarity, effect size, budget constraints) and uses probabilistic inference to determine optimal field values that satisfy multiple competing requirements, transforming a complex multi-parameter optimization problem into manageable probabilistic queries.
Data Source
AI summary
The present disclosure relates to methods and systems that provide querying and analysis of clinical trials using probabilistic graphical models. The methods and systems train a probabilistic graphical model using clinical trial data and use the probabilistic graphical model to perform inferences in response to queries for clinical trials. The methods and systems use the probabilistic graphical model to handle multimodal datatypes of the clinical trial data and predict multiple attributes of the clinical trial for an input query.