Topology-Based Clinical Data Mining for Trial Subject Grouping
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Solution Overview
Problem
Current clinical data analysis methods focus on univariate relationships, lacking comprehensive data integration and visualization tools to fully understand complex clinical trial datasets, leading to incomplete or misleading views of trial outcomes.
Innovation Solution
A topology-based clinical data mining system that combines biostatistics, machine learning, and data visualization, processing clinical datasets to generate metric graphs and perform statistical analysis, allowing for interactive exploration and identification of hidden patterns among trial subjects with similar outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard biostatistical methods are used to confirm hypotheses, then the reliability of hypothesis confirmation is improved, but the difficulty of selecting relevant hypotheses increases due to the large number of possible hypotheses
Solution Approach 1:
The system performs preliminary data exploration and pattern recognition using topology-based methods before formal hypothesis testing. This preliminary action identifies promising hypotheses and relationships, reducing the complexity of hypothesis selection while maintaining reliable statistical confirmation for the most relevant hypotheses.
Solution Approach 2:
The patent introduces an intermediary visualization system that bridges data exploration and formal statistical analysis. This intermediary layer helps researchers navigate the large hypothesis space by visually identifying patterns and relationships, making hypothesis selection more manageable while preserving the reliability of subsequent statistical tests.
2Loss of information
If comprehensive analysis of all clinical trial data is performed, then the completeness of understanding trial outcomes is improved, but the complexity of data analysis increases
Solution Approach 1:
The patent segments the complex clinical trial data into multiple visualizable dimensions and perspectives. By dividing the data analysis into separate visual components (different graph layouts, projections, and views), the system maintains information completeness while reducing the complexity of analyzing all data simultaneously.
Solution Approach 2:
The patent transforms complex multidimensional clinical data into visual representations across multiple dimensions. By projecting data into different graphical dimensions and layouts, the system enables comprehensive analysis without overwhelming complexity, allowing researchers to explore relationships from multiple angular perspectives.
3Measurement precision
If focus is placed on a specific single outcome in isolation, then the depth of analysis for that outcome is improved, but the accuracy of understanding complex clinical settings deteriorates
Solution Approach 1:
The patent creates a multi-functional visualization system that can simultaneously analyze specific outcomes and their relationships to other variables. The same graphical framework enables both focused deep analysis of individual outcomes and broader contextual understanding, maintaining both precision and reliability through integrated visualization.
Data Source
AI summary
Methods and systems for topology-based clinical data mining are provided. An example system includes a pre-processing module to process the clinical datasets to generate a table of outcomes and a table of predictors of trial subjects. The system includes a graph construction module to generate metric graphs based on the table of outcomes. The metric graphs include nodes representing the subjects and edges selectively connecting the nodes according to pre-determined criteria. The graph construction module may select a graph of interest from the metric graphs and generate a compressed version of the graph of interest. The system may further include an interactive visualization module to display a graphical representation of the graph of interest or the compressed version, receive selection of groups of the trial subjects, automatically highlight groups of related subjects, and perform, using the table of predictors, a statistical analysis of predictors of subjects within the selected groups.