Cox Regression Mortality Risk Stratification
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Solution Overview
Problem
Current methods for estimating mortality risk in insurance underwriting are limited by cross-correlation of laboratory analytes and physical characteristics, leading to cross-attribution of effects and non-additivity of results, which results in an overestimation of mortality risk due to implicit attribution of correlated variables.
Innovation Solution
The use of a Cox proportional hazards multivariate regression model that incorporates multiple physical characteristics and analyte levels, with coefficients determined from a large dataset, to calculate relative mortality risk, adjusting for age, gender, and stratifying by tobacco use, and accounting for non-linear relationships between variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple laboratory analytes and physical characteristics are used to estimate mortality risk, then the comprehensiveness of risk assessment is improved, but cross-correlation between variables causes overestimation of mortality risk
Solution Approach 1:
The patent segments the risk assessment by stratifying the peer group into subgroups based on tobacco use status (smokers vs. non-smokers) and other key variables. This segmentation prevents cross-attribution of effects between correlated variables by ensuring that comparisons are made within homogeneous subgroups rather than across the entire heterogeneous population, thereby resolving the contradiction between comprehensive assessment and reliable estimation.
Solution Approach 2:
The patent transforms the risk estimation approach by changing the parameter of comparison from absolute risk values to relative risk ratios within stratified groups. By expressing mortality risk as a ratio compared to the median risk within the same peer group stratification, the method accounts for correlations between variables without overestimating risk, as the comparison is normalized within each stratified subgroup.
2Device complexity
If individual assays or restricted groups of assays are used, then the complexity of the assessment process is reduced, but the precision of mortality risk estimation is limited
Solution Approach 1:
The patent creates a universal multivariate model that can simultaneously process multiple types of input variables (laboratory analytes, physical characteristics, demographic information) through a single integrated framework. The Cox proportional hazards model serves as a multi-functional tool that handles diverse variables while accounting for their interrelationships, thereby improving estimation precision without proportionally increasing operational complexity.
Solution Approach 2:
The model performs self-adjustment through automatic stratification by key variables such as age, sex, and tobacco use status. The system automatically identifies and controls for confounding factors within the data structure itself, reducing the need for manual adjustment and complex procedural steps while maintaining high estimation precision across diverse populations.
3Ease of operation
If correlated variables are implicitly attributed to individual risk factors, then the simplicity of interpretation is maintained, but the accuracy of risk attribution is compromised
Solution Approach 1:
The patent adds the dimension of stratification by tobacco use status and other key variables to the risk assessment framework. This dimensional expansion allows the model to separate correlated variables into distinct analytical layers, enabling accurate attribution of risk to specific factors within each stratum while maintaining overall interpretability through the structured hierarchical approach.
Data Source
AI summary
A method for determining the relative mortality risk of an individual as compared to their age/sex/tobacco-use peers. This relative mortality risk may be used in underwriting a life or medical insurance policy.