Graph-Based Clinical Data Geometry Discovery
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Solution Overview
Problem
Current methods for analyzing clinical trial data focus on univariate relationships, lacking comprehensive data integration and visualization tools, which can lead to an incomplete or misleading view of complex clinical trial settings.
Innovation Solution
The implementation of graph-based methods for discovering the geometry of clinical data, combining clinical biostatistics, topological data analysis, machine learning, and data visualization, to identify hidden patterns and communities of clinical trial subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard biostatistical methods are used to confirm hypotheses, then measurement precision is improved, but device complexity increases due to the large number of possible hypotheses to explore
Solution Approach 1:
The patent segments the complex hypothesis exploration process into distinct phases: (1) generating hypotheses from clinical trial data, (2) ranking hypotheses using multiple criteria, and (3) confirming selected hypotheses using biostatistical methods. This segmentation reduces system complexity by breaking down the overwhelming task of exploring all possible hypotheses into manageable stages, while still maintaining measurement precision through rigorous statistical validation of the most promising hypotheses.
2Loss of information
If comprehensive data integration tools are implemented to improve understanding of the entire dataset, then information completeness is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary ranking system that mediates between the raw clinical trial data and the comprehensive analysis tools. The system generates hypotheses, ranks them using multiple criteria (relevance, novelty, feasibility), and presents a curated set of top-ranked hypotheses for further analysis. This intermediary layer reduces tool complexity by filtering and organizing data before it reaches the comprehensive analysis stage, while still achieving information completeness through the multi-criteria evaluation process.
3Measurement precision
If graph-based methods are used to discover hidden patterns in clinical data, then measurement precision is improved, but device complexity increases due to generating and evaluating multiple metric graphs
Solution Approach 1:
The patent applies preliminary action by pre-ranking metric graphs using multiple criteria before performing comprehensive pattern discovery analysis. The system generates multiple metric graphs from clinical trial data, ranks them based on relevance, robustness, and other criteria, and then focuses detailed analysis on the top-ranked graphs. This preliminary ranking reduces graph processing complexity by eliminating the need to exhaustively analyze all possible graphs, while still maintaining measurement precision through the multi-criteria evaluation framework.
Data Source
AI summary
A system and method for graph-based discovery of geometry of clinical data are disclosed. The method includes receiving vectors of outcomes of trial subjects; generating, based on the vectors of outcomes, a plurality of metric graphs such that each of the metric graphs includes a same set of nodes corresponding to the vectors of outcomes and the nodes are selectively connected based on a first criterion, the first criterion being based on a set of parameters, the set of parameters being uniquely selected, from a plurality of sets of parameters, for each of the metric graphs; determining, based on the plurality of metric graphs and a second criterion, an aggregated graph and a subset of the plurality of metric graphs; selecting, from the subset of the plurality of metric graphs and based on a third criterion, optimal and most representative graphs; and displaying a graphical representation of optimal graphs.