White-Coat Effect Prediction Model Using Iterative Parameter Selection
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Solution Overview
Problem
Conventional methods for detecting white-coat hypertension and uncontrolled hypertension are time-consuming and labor-intensive, requiring home blood pressure monitoring or 24-hour ambulatory blood pressure monitoring.
Innovation Solution
A computing device with a processor and storage medium that uses a machine learning algorithm and model-explanation tool to establish a prediction model by selecting and supplementing characteristic parameters, ultimately creating a candidate model for predicting the probability of white-coat effect through iterative validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If home BP monitoring or 24-hour ambulatory BP monitoring is used to detect white-coat hypertension, then diagnosis accuracy is improved, but time consumption and labor intensity increase
Solution Approach 1:
The patent applies preliminary action by using a prediction model to pre-screen patients and identify those likely to have white-coat hypertension before implementing the time-consuming home BP monitoring or 24-hour ambulatory BP monitoring. The model uses readily available clinical data (age, sex, BMI, office BP, drug usage) to predict probability, allowing clinicians to prioritize only high-risk patients for further monitoring, thus reducing overall time consumption while maintaining diagnosis accuracy for those who need it.
Solution Approach 2:
The patent introduces an intermediary prediction model that acts as a mediator between initial office BP measurement and definitive diagnosis through home/ambulatory monitoring. This intermediary tool processes clinical data and generates probability scores, serving as a filtering layer that reduces the number of patients requiring resource-intensive monitoring while preserving diagnostic accuracy for true positive cases.
2Measurement precision
If home BP monitoring or 24-hour ambulatory BP monitoring is used to detect white-coat hypertension, then diagnosis accuracy is improved, but labor intensity increases
Solution Approach 1:
The prediction model performs preliminary identification of high-risk patients using automated processing of routine clinical data, eliminating the need for manual review and prioritization by healthcare workers. By automatically calculating probabilities and flagging high-risk cases, the system significantly reduces labor intensity associated with organizing and managing home/ambulatory monitoring programs while maintaining high diagnostic accuracy.
Solution Approach 2:
The system enables self-service by allowing the prediction model to automatically process clinical data, generate probability assessments, and identify candidates for monitoring without requiring manual intervention. The automated workflow reduces the burden on healthcare workers, transforming a labor-intensive process into a more efficient self-organizing system that maintains diagnostic rigor.
3Measurement precision
If multiple characteristic parameters are included in the prediction model, then prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The patent applies the extraction principle by using the model-explanation tool to identify and extract only the most impactful characteristic parameters from the available clinical data. Rather than incorporating all possible parameters, the system selectively extracts the subset that contributes most to prediction accuracy, thereby maintaining high performance while reducing model complexity and improving interpretability.
Solution Approach 2:
The patent implements local quality by assigning different levels of importance to different characteristic parameters based on their impact values. The model-explanation tool identifies which parameters (e.g., age, office BP, drug usage) have greater local impact on prediction outcomes, allowing the system to focus computational resources on the most relevant features rather than treating all parameters equally, thus optimizing the balance between accuracy and complexity.
Data Source
AI summary
A method includes: obtaining impact values for characteristic conditions; selecting training data subsets respectively from training data sets according to the impact values; obtaining a candidate model and an evaluation value based on the training data subsets; supplementing the training data subsets according to the impact values; obtaining another candidate model and another evaluation value based on training data subsets thus supplemented; repeating the step of supplementing the training data subset, and the step of obtaining another candidate model and another evaluation value based on the training data subsets thus supplemented; and selecting one of the candidate models as a prediction model based on the evaluation values.