Causal Inference Engine for Correlating Heterogeneous Research Models
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Solution Overview
Problem
Existing scientific research, particularly in clinical trials, faces challenges in systematically correlating and leveraging data from different experiments due to heterogenous models with different protocols and data formats, hindering the extraction of maximum value and posing humanitarian implications for untreated illnesses.
Innovation Solution
A causal inference engine utilizing category theory and dimensional flattening techniques to relate heterogenous models by converting data into normalized attribute vectors, identifying functors and natural transformations, and generating graph and category databases for causal inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from different experiments are systematically correlated using heterogenous models, then the accuracy and quality of research results are improved, but the complexity of data integration and model correlation increases
Solution Approach 1:
The patent transforms heterogeneous experimental data into a unified parameter space by identifying and standardizing key parameters across different models. This allows data from diverse sources to be correlated through parameter mapping and transformation, resolving the contradiction by changing the state of data representation rather than manually integrating complex heterogeneous models.
Solution Approach 2:
The patent creates a universal data correlation framework that can handle multiple types of experimental data and models through a single integrated system. This universal approach enables automatic correlation across heterogenous models without requiring separate integration processes for each model type, reducing overall system complexity while maintaining accuracy.
2Quantity of substance
If data from multiple experiments are correlated to increase sample size, then the statistical power and generalizability are improved, but the difficulty of determining applicability between experiments increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically evaluate the applicability of experimental data to target research questions. The system provides feedback on data quality, relevance, and compatibility metrics, enabling researchers to systematically determine whether correlated data should be included in the analysis, thus reducing the difficulty of applicability assessment.
Solution Approach 2:
The patent performs preliminary filtering and validation of experimental data before correlation to pre-establish applicability criteria. By conducting preliminary assessments of data compatibility, quality standards, and model appropriateness before integration, the system reduces the complexity of determining applicability during the actual correlation process.
Data Source
AI summary
A causal inference engine relies on a category database and a graph database. Via a loader, a category generator, and a spatial web generator, input data is received in disparate formats and converted into a normalized attribute vector comprising a mathematical model, experiment, experimental data, and miscellaneous attributes. The normalized attribute vectors are then loaded into a category database and a graph database. Specifically, the loader makes use of a multi-formal combinatorial parser, and an ontology store to convert the different data and formats into normalized attribute vectors. The category generator reviews mathematical model attributes in the normalized attribute vectors to associate vectors into mathematical categories. The spatial web generator performs similarity scores in the attributes of the vectors to determine placement in a graph database. The data in the category database and the graph database are then utilized by the causal inference engine to perform inferences.