Multi-dimensional Lookup Table for Dynamic Reference Intervals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for generating reference intervals are inaccurate due to small population sample sizes and failure to account for interplay between multiple health or risk parameters, leading to inadequate contextualization of data values and potential misclassification of health status.
Innovation Solution
A computer-implemented system generates reference intervals based on large datasets, using a multi-dimensional lookup table that compares multiple health or risk parameters to determine contextualized reference intervals, incorporating machine learning algorithms to identify overlaps and generate dynamic intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical modeling techniques are used to generate reference intervals, then the process is simple and quick, but the accuracy and reliability of the reference intervals are compromised due to small population sample sizes and erroneous assumptions
Solution Approach 1:
The patent transitions from traditional one-dimensional statistical modeling to multi-dimensional data analysis by incorporating multiple health parameters (e.g., age, sex, race, multiple laboratory values) simultaneously. This dimensional expansion allows the system to capture complex inter-parameter relationships and generate more accurate reference intervals that reflect real-world population variability.
Solution Approach 2:
The patent creates a composite analytical approach by integrating multiple data sources, parameter types, and analysis methods into a unified reference interval generation system. This composite structure combines demographic data, clinical parameters, and outcome data to produce robust reference intervals that leverage the strengths of each component while mitigating individual limitations.
2Reliability
If traditional statistical methods assume fixed data distributions, then the analysis is computationally efficient, but the models fail to objectively account for relevant information and produce inaccurate results
Solution Approach 1:
The patent implements dynamic reference intervals that adapt to individual patient characteristics and population variations. Rather than relying on fixed statistical distributions, the system dynamically generates reference intervals based on multi-dimensional parameter relationships, allowing the reference ranges to flex and adjust according to specific demographic and clinical contexts.
Solution Approach 2:
The patent transforms the analysis by changing from fixed distributional assumptions to data-driven parameter relationships. The system identifies and utilizes actual parameter interrelationships from large population datasets, allowing the reference interval generation to be driven by observed data patterns rather than predetermined statistical models.
3Measurement precision
If large population datasets are analyzed with multiple parameters, then the reference intervals become more accurate and individualized, but the data processing complexity and computational resources required increase significantly
Solution Approach 1:
The patent segments the complex multi-dimensional analysis into manageable components by organizing parameters into hierarchical groups (demographic parameters, clinical parameters, laboratory parameters). This segmentation allows the system to process large datasets systematically, analyzing parameter relationships in structured stages rather than attempting to evaluate all parameters simultaneously.
Solution Approach 2:
The patent introduces computational intermediaries in the form of algorithmic processing layers that mediate between raw multi-dimensional data and final reference interval outputs. These intermediary processing steps include data normalization, parameter relationship identification, and iterative reference interval calculation, which break down the complex analysis into sequential manageable operations.
Data Source
AI summary
Described herein are systems and methods for multi-dimensional analysis of complex data sets to generate multi-factorial overlap intervals used in lookup tables to classify input data. Also disclosed are systems and methods for provisioning searchable databases comprising the multi-factorial overlap intervals through a distributed network for remote access.