Cyclic Experimental Database for Clinical Data Analysis
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Solution Overview
Problem
Current statistical analysis methods for clinical data are time-consuming, labor-intensive, and lack the ability to perform global analysis, relying on subjective judgments and finite permutations, which limits the comprehensive evaluation of clinical data.
Innovation Solution
A method and system for managing experimental data that classifies data into baseline indicators, evaluation indicators, intervention methods/groups, and time, enabling complete permutation and combination analysis through a cyclic experimental database, allowing for global statistical analysis and visualization of results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional statistical software (SPSS, SAS) is used for finite permutation and combination analysis, then analysis can be performed for one permutation and combination, but the process is time-consuming and labor-consuming when multiple permutations and combinations need to be analyzed
Solution Approach 1:
The patent segments the clinical data into four distinct classes (baseline indicators B, evaluation indicators E, intervention methods/groups G, and time T), allowing independent processing and combination of each class. This segmentation enables systematic generation of multiple permutation and combination results without manual reconfiguration, significantly improving analysis throughput while reducing time consumption.
Solution Approach 2:
The patent performs preliminary classification and organization of clinical data into the BEGT structure before analysis. By pre-establishing the data framework with all possible permutations and combinations, the system eliminates the need for repeated manual setup when analyzing multiple scenarios, thereby increasing productivity and reducing analysis time.
2Adaptability or versatility
If finite permutations and combinations are used for subgroup analysis, then analysis can be completed for specific cases, but global analysis of all possible combinations is impossible
Solution Approach 1:
The patent creates a universal BEGT classification framework that can handle any permutation and combination of clinical data attributes. This unified structure allows the system to perform both specific subgroup analysis and comprehensive global analysis across all possible combinations, greatly enhancing adaptability without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing data along four independent axes (B, E, G, T) rather than traditional single-dimension analysis. This multi-dimensional framework enables systematic exploration of all possible combinations, expanding the scope from limited case-specific analysis to comprehensive global analysis.
3Measurement precision
If conventional statistical analysis methods are used, then results can be obtained for selected groups, but judgments on results are highly subjective
Solution Approach 1:
By segmenting clinical data into four standardized classes (B, E, G, T) with clear definitions and stratification criteria, the patent establishes an objective framework for data classification. This structured approach reduces subjectivity in result interpretation while maintaining ease of operation through systematic processing rules.
Data Source
AI summary
A method/system for managing experimental data, a computer readable storage medium, and a device are provided. The method includes: recording the managing experimental data, and preprocessing the experimental data, to obtain at least two preprocessed experimental arrays; selecting one element from each of two selected preprocessed experimental arrays according to an analysis requirement, and combining the elements to form a cyclic experimental database, the cyclic experimental database including several combination data; performing cyclic statistical analysis on the combination data in the cyclic experimental database, to obtain a cyclic statistical result corresponding to retrieved combination data.