Clinical Decision Support Algorithm Covariate Shift Adjustment
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Solution Overview
Problem
Commercial electronic clinical decision support (CDS) devices face accuracy degradation due to covariate shift, where the statistics of the covariates in the customer population differ from those in the training population, making it challenging to provide accurate medical condition predictions without collecting sensitive patient data or exposing proprietary algorithms.
Innovation Solution
The solution involves adjusting the CDS algorithm using marginal probability distributions for covariate shift, which are computed from population-level statistics, allowing for updates at the customer end without requiring patient-level data or exposing vendor proprietary information, and employing a covariate shift predictor to refine predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If commercial electronic CDS devices use training data from general populations, then the device can be broadly applicable, but prediction accuracy degrades for specific hospital populations due to covariate shift
Solution Approach 1:
The patent changes the parameters of the training data by adjusting covariate distributions to match target hospital demographics. This is achieved through reweighting training samples based on the ratio of target population probabilities to source population probabilities, thereby adapting the model to specific hospital populations while maintaining broad applicability
Solution Approach 2:
The patent segments the population into different demographic groups based on covariates such as age, gender, and ethnicity. By creating separate demographic strata and applying targeted reweighting to each segment, the system achieves both broad applicability across multiple populations and high accuracy within each specific demographic group
2Measurement precision
If patient-level data is collected to adjust for covariate shift, then prediction accuracy improves, but patient privacy and confidentiality are compromised
Solution Approach 1:
The patent introduces demographic summary statistics as an intermediary between patient-level data and model training. Instead of using sensitive patient-level information, the system collects aggregated demographic data (mean age, gender distribution, etc.) from hospitals and uses these summaries to compute reweighting factors, thereby achieving accuracy improvement without exposing patient privacy
Solution Approach 2:
The patent creates a synthetic representation of the target population by generating weighted training samples that replicate the demographic characteristics of the hospital population. This synthetic copy allows the model to adapt to specific populations without accessing or storing actual patient data, thus maintaining privacy while improving accuracy
3Measurement precision
If proprietary algorithms are exposed for customer-side adjustment, then accuracy improves through local adaptation, but vendor intellectual property is compromised
Solution Approach 1:
The patent extracts only the necessary demographic parameters and reweighting logic from the proprietary algorithm, leaving the core predictive model intact and proprietary. The customer-side implementation receives pre-computed reweighting factors based on demographic summaries, enabling local adaptation without exposing the vendor's proprietary algorithmic details
4Measurement precision
If comprehensive patient data is collected for training, then model accuracy improves, but computational requirements and data storage needs increase
Solution Approach 1:
The patent extracts only the essential demographic summary statistics (mean, variance, distribution parameters) from comprehensive patient data, discarding the bulk of individual patient records. This extraction approach maintains model accuracy by preserving key population characteristics while dramatically reducing data storage requirements and computational overhead
Data Source
AI summary
An electronic clinical decision support (CDS) device (10) employs a trained CDS algorithm (30) that operates on values of a set of covariates to output a prediction of a medical condition. The CDS algorithm was trained on a training data set (22). The CDS device includes a computer (12) that is programmed to provide a user interface (62) for completing clinical survey questions using the display and the one or more user input devices. Marginal probability distributions (42) for the covariates of the set of covariates are generated from the completed clinical survey questions. The trained CDS algorithm is adjusted for covariate shift using the marginal probability distributions. A prediction of the medical condition is generated for a medical subject using the trained CDS algorithm adjusted for covariate shift (50) operating on values for the medical subject of the covariates of the set of covariates.