Reference Interval Generation Using Multivariate Health Data Segmentation
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Solution Overview
Problem
Traditional reference intervals for health or risk parameters are inaccurate due to small population sample sizes and failure to consider interplay between multiple health or risk parameters, leading to inadequate contextualization and potential misclassification of health status.
Innovation Solution
The systems and methods generate reference intervals based on large datasets, contextualizing multiple health or risk parameters and associating them with mortality or adverse outcome rates, using a computer-implemented method to create a database with lookup tables that define normal or abnormal values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical fitting methods are used to generate reference intervals, then the process is simple and quick, but the accuracy and reliability of the reference intervals deteriorate due to small sample sizes and lack of contextualization
Solution Approach 1:
The patent segments the population data into multiple subgroups based on demographic characteristics (age, sex, ethnicity) and health status. This segmentation allows for the generation of more accurate reference intervals for each specific subgroup, improving measurement precision by accounting for population heterogeneity rather than using a single reference interval for all individuals.
Solution Approach 2:
The patent transitions from traditional univariate statistical analysis to multivariate analysis by incorporating multiple health parameters simultaneously. This dimensional expansion allows the system to contextualize parameters within their interrelationships, significantly improving the accuracy of reference intervals by considering the complex interactions between different health metrics.
2Reliability
If small population samples are used to generate reference intervals, then the data collection process is faster and less resource-intensive, but the representativeness and generalizability of the reference intervals worsen
Solution Approach 1:
The patent performs preliminary filtering and validation of large datasets to identify and exclude individuals with known health conditions or medications that could confound reference interval generation. This preliminary action on large populations allows the system to efficiently extract reliable reference data without requiring time-consuming prospective studies, thus improving representativeness while managing data collection time.
Solution Approach 2:
The patent dynamically adjusts inclusion and exclusion criteria based on the specific parameter being analyzed and the population characteristics. By changing parameters such as age ranges, health status definitions, and parameter combinations, the system can efficiently generate accurate reference intervals for different populations without requiring separate large-scale studies for each scenario.
3Measurement precision
If single health parameters are analyzed in isolation, then the analysis process is simpler, but the contextualization and diagnostic accuracy worsen due to absence of interparameter relationships
Solution Approach 1:
The patent merges multiple health parameters into integrated analytical models that evaluate their interrelationships. By combining parameters such as lipid profiles, glucose levels, and inflammatory markers into unified reference frameworks, the system improves diagnostic accuracy by capturing the synergistic and antagonistic relationships between different health metrics that cannot be detected when parameters are analyzed in isolation.
Data Source
AI summary
Described herein are systems and methods for generating health or risk parameter reference intervals for use in healthcare or any other application of risk/attribute measurement. The reference intervals generated by the systems and methods described herein are based on direct analysis and evidence-based models of human and/or other living organism data from a population of individuals including shared feature(s) and outcome data such as vital status. The direct analysis and evidence-based models of human and/or other living organisms data utilize shared common risk parameter feature(s) and outcome data and identify relationships of two or more health or risk parameters relative to one another. These methods also apply to the measurement of risk and/or other attributes of any living organism or nonliving objects.