Graph-Based Clinical Data Analysis for Hidden Pattern Discovery
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Solution Overview
Problem
Current methods for analyzing clinical trial data focus on univariate relationships, lacking integration and visualization tools to comprehensively understand datasets, leading to incomplete or misleading views of complex settings and making it difficult to select relevant hypotheses.
Innovation Solution
The implementation of graph-based methods that combine clinical biostatistics, topological data analysis, and machine learning for interactive visualization, allowing for the discovery of hidden patterns in clinical datasets by generating metric graphs and identifying communities of trial subjects with similar outcomes.
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 introduces topological data analysis and graph-based visualization as intermediary tools between raw clinical data and biostatistical hypothesis testing. These intermediaries transform complex multivariate data into intuitive geometric representations and candidate hypothesis structures, reducing the burden on researchers to manually navigate the vast hypothesis space while maintaining measurement precision through systematic exploration
2Loss of information
If comprehensive analysis of clinical trial datasets is performed, then information completeness is improved, but loss of time increases due to the challenge of examining all possible relationships
Solution Approach 1:
The patent applies preliminary action by using topological data analysis to pre-process and structure clinical trial data into metric graphs and candidate communities before full analysis. This preliminary organization identifies potential relationships and groups subjects with similar outcomes in advance, allowing researchers to focus subsequent biostatistical analysis on promising candidates rather than exhaustively examining all possible relationships
Solution Approach 2:
The patent segments the comprehensive analysis task into distinct phases: (1) topological data analysis to generate metric graphs and identify candidate communities, (2) visualization to present structured relationships, and (3) targeted biostatistical analysis of identified candidates. This segmentation breaks down the overwhelming comprehensive analysis into manageable steps that maintain information completeness while reducing total analysis time
3Ease of operation
If univariate relationships are examined in isolation, then ease of operation is improved, but loss of information occurs due to incomplete or misleading views of complex settings
Solution Approach 1:
The patent transitions from univariate analysis to multivariate analysis by introducing geometric dimensions through metric graphs. Subjects, outcomes, and predictors are represented as points and relationships in multidimensional space, allowing simultaneous visualization of multiple variables and their interrelationships. This dimensional transformation preserves analytical simplicity through visual intuition while capturing contextual information that univariate methods miss
Data Source
AI summary
Methods and systems for graph-based discovery of geometry of clinical data are provided. An example method includes receiving vectors of outcomes of trial subjects, generating, based on the vectors of outcomes, a plurality of metric graphs, each of the metric graphs including a set of nodes corresponding to the vectors of outcomes and a set of edges, performing an automatic search to identify communities of nodes in the optimal graph, displaying a graphical representation of the optimal graph and highlighting nodes in the graphical representation, the nodes corresponding to the community of nodes. Generating the set of edges includes selecting metrics and projection rules to obtain projections of the vectors of outcomes, and selectively connecting nodes based on determination that projections of corresponding vectors of outcomes belong to the same domain of a set of overlapping domains and a certain cluster within the domain.