Health Risk Prediction Uncertainty Quantification
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Solution Overview
Problem
Existing disease risk prediction models fail to accurately quantify prediction uncertainty, particularly for patients with missing or defective features, leading to overconfident predictions and inadequate clinical decision support.
Innovation Solution
A system that utilizes multiple risk prediction models and a distillation model to generate a single health risk score and confidence interval, decomposing uncertainty into model and input uncertainty, and assigns feature impact scores to identify missing or defective features, thereby improving prediction accuracy and clinical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed threshold is applied to the risk score for clinical decision support, then the model provides simple and actionable guidance, but the model produces overconfident predictions for patients with risk scores close to the decision boundary or with missing features
Solution Approach 1:
The patent segments prediction uncertainty into two distinct components: model uncertainty (from parameter estimation) and input uncertainty ( from missing/defective features). This segmentation allows the system to provide differentiated confidence assessments while maintaining simple threshold-based decision support, resolving the contradiction between ease of operation and prediction reliability.
Solution Approach 2:
The patent introduces confidence intervals as an intermediary element between the risk score and the fixed threshold decision rule. This intermediary provides nuanced uncertainty information without complicating the clinical decision-making process, allowing clinicians to interpret both the risk score and its confidence interval alongside the threshold recommendation.
2Measurement precision
If Bayesian approaches are used to estimate model uncertainty through prior and posterior distributions, then the model uncertainty can be characterized, but the approach cannot be easily applied to boosting and tree-based models
Solution Approach 1:
The patent uses bootstrapping to create multiple copies of the training dataset through sampling with replacement, then trains multiple model instances on these copied datasets. The empirical distribution of parameters across these copied models characterizes model uncertainty without requiring prior distributions or posterior computation, making the approach compatible with any prediction model including boosting and tree-based models.
3Measurement precision
If multiple imputation is applied to estimate input uncertainty from missing features, then the overall prediction uncertainty can be quantified, but the contribution of each individual feature to uncertainty cannot be identified
Solution Approach 1:
The patent segments the overall input uncertainty into feature-level contributions by computing the feature impact score for each missing or defective feature. This segmentation identifies which specific features most affect prediction uncertainty, enabling targeted data collection strategies while maintaining the comprehensive uncertainty quantification provided by multiple imputation.
4Ease of operation
If the system reports only the risk score without uncertainty information, then the output is simple and quick to interpret, but clinicians cannot assess the reliability of predictions for patients with missing features
Solution Approach 1:
The patent segments the confidence interval into two components: model uncertainty and input uncertainty. This segmentation provides clinicians with differentiated information about the sources of uncertainty, maintaining interpretability while enabling assessment of prediction reliability even when features are missing.
Solution Approach 2:
The patent introduces the confidence interval as an intermediary between the risk score and the clinical decision threshold. This intermediary preserves the simplicity of threshold-based decision-making while providing additional reliability information that helps clinicians assess prediction confidence without complicating the core decision process.
Data Source
AI summary
A method for risk analysis, comprising: (i) receiving a plurality of features about a subject; (ii) analyzing the features using risk prediction models to generate risk scores; (iii) determining, using a distillation model, mean and variance among the risk scores; (iv) generating a single risk score and a risk score confidence interval; (v) determining, based on a feature impact score for each feature, an effect of one or more missing or defective features on the generated risk score confidence interval, wherein the system identifies a missing or defective feature for reporting if that feature would narrow the generated risk score confidence interval if it were not missing or not defective; (vi) generating a report comprising the single risk score and the risk score confidence interval, and further comprising at least one or more of the identified missing or defective features; and (vii) providing the report.