Disease Risk Score Calculation Using Relative and Incidence Factors
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Solution Overview
Problem
Current methods for determining disease risk scores in the medical sector face challenges due to the scarcity and bias of medical data, making it difficult to accurately estimate risks for individuals outside the initial prospective cohort, leading to overestimation or underestimation when exporting results to different populations.
Innovation Solution
A method that selects a prospective cohort from an initial population, estimates individual relative risk scores based on parameters, and multiplies these scores by the incidence of pathologies in the final population to calculate absolute risk scores, allowing for flexible adaptation to different populations and therapeutic treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a prospective cohort is used to estimate disease risk scores, then the accuracy of risk estimation is improved, but the applicability to different populations deteriorates due to cohort-specific biases
Solution Approach 1:
The method segments the risk estimation process into two independent components: (1) a population-specific incidence rate component that captures local disease occurrence, and (2) a relative risk component that captures individual risk factors. This segmentation allows the relative risk model to be universally applied across different populations while each population contributes its specific incidence rates, thereby maintaining both accuracy and adaptability.
Solution Approach 2:
The invention introduces an intermediary adjustment mechanism that uses population-specific incidence rates as a mediator between the universal relative risk model and individual populations. This intermediary component allows the model to adapt to different populations without requiring population-specific risk factor models, resolving the contradiction between universal applicability and population-specific accuracy.
2Quantity of substance
If medical data from hospital records is used, then the availability of data is improved, but the representativeness of the data deteriorates due to selection bias toward sick populations
Solution Approach 1:
The method extracts the relative risk information from cohort data while separating it from the incidence rate information. By taking out the relative risk component (which reflects individual risk factors) from the population-specific incidence data, the method can combine these components in a way that maintains representativeness while utilizing available data effectively.
Solution Approach 2:
The invention changes the parameter used for risk estimation from absolute risk (which requires population-specific data) to relative risk (which can be standardized). This parameter change allows the use of cohort data with different population compositions while maintaining reliability through the standardized relative risk calculation, then adapting to specific populations through incidence rate multiplication.
3Measurement precision
If a large prospective cohort is used to ensure sufficient statistical power, then the precision of risk estimation is improved, but the scarcity of such cohorts worsens the availability of population-specific data
Solution Approach 1:
The method creates a universal relative risk model that can serve multiple populations simultaneously. Instead of requiring separate large cohorts for each population, the same relative risk model can be applied across different populations by multiplying with population-specific incidence rates. This multi-functionality increases the availability of usable data while maintaining precision through the universal model's statistical power.
Solution Approach 2:
The invention merges the strengths of large universal cohorts with population-specific incidence data. By combining the statistically powerful relative risk estimates from large cohorts with population-specific incidence rates, the method achieves both precision (from the large cohort) and population-specific accuracy (from the incidence data), effectively creating a virtual large cohort for each population without requiring actual large population-specific cohorts.
Data Source
AI summary
The invention relates to a method for determining risk scores of diseases for a given individual belonging to a final population, that comprises the following steps: selecting a prospective group from an initial population different from the final population; estimating a relative individual risk score modulus of the individual from parameters and variables of the prospective group, said modulus including at least one risk score associated with at least one disease variable; multiplying each risk score of the relative individual modulus by the impact of each disease in the final population of the individual. The invention can be used for a more reliable estimation of the disease risk scores for individuals belonging to populations different from the initial population of the prospective group.