Causal Inference Engine for Heterogeneous Clinical Trial Data
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Solution Overview
Problem
Current research and development efforts face challenges in systematically correlating and leveraging data from different experiments and trials, particularly in clinical trials, due to heterogenous models, protocols, and data formats, which hinders the extraction of maximum value from experimental results and poses humanitarian implications in drug discovery and testing.
Innovation Solution
A causal inference engine utilizing category theory and dimensional flattening techniques, such as the spatial web, to relate heterogenous clinical trials by converting data into normalized attribute vectors, identifying categories, functors, and natural transformations, and performing causal inferences across graph databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from different experiments and trials are systematically correlated using heterogenous models and protocols, then the accuracy and quality of research results are improved, but the complexity of data integration and transformation increases
Solution Approach 1:
The patent transforms heterogeneous experimental data into a unified parameter space by identifying and standardizing key parameters across different experiments. This allows data from diverse sources to be correlated by comparing standardized parameters rather than dealing with raw heterogeneous data structures, thereby improving accuracy while managing complexity through parameter normalization.
Solution Approach 2:
The patent creates a universal data correlation framework that can handle multiple types of experimental data from different sources simultaneously. The system is designed to work with heterogenous models, protocols, and data formats through a common interface, enabling one system to serve multiple functions across diverse research domains without requiring separate integration approaches for each data type.
2Adaptability or versatility
If transformations are applied to correlate materials and data from different experiments, then the applicability of results across experiments is improved, but the difficulty of determining appropriate transformations increases
Solution Approach 1:
The patent introduces an intermediary correlation layer that sits between heterogeneous data sources and the analysis system. This intermediary layer automatically performs transformations and mappings based on predefined relationships between different experimental models and protocols, shielding users from the complexity of transformation determination while enabling broad applicability of results across different experiment types.
3Productivity
If experimental data is leveraged across multiple research efforts, then the productivity and efficiency of scientific research are improved, but the loss of time for data correlation and validation increases
Solution Approach 1:
The patent performs data correlation, transformation, and validation operations in advance, before the actual research analysis is needed. By pre-processing and pre-correlating experimental data into standardized formats with established relationships, the system eliminates the need for time-consuming data correlation activities when research efforts begin, thereby improving productivity without sacrificing correlation quality.
Data Source
AI summary
A causal inference engine makes inferences based on a vector of attributes in a standardized format called a normalized attribute vector. Candidate correlations between the normalized attribute vectors are made via a machine learning algorithm operating on the attributes of the normalized attribute vectors. The candidate correlations are then validated against a set of known mechanisms, in some cases selected by making use of mathematical category theory. Where a candidate correlation is shown to be similar to a mechanism, or composition of mechanisms, the candidate correlation is validated as being causative rather than just a correlation. Where causation can be shown to have a confidence above a predetermined threshold, the correlation is then stored as to be used to validate other correlations in future processing.