Patient Risk Model Selection Using Population-Specific Cost Functions
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Solution Overview
Problem
Healthcare facilities face challenges in selecting an optimal combination of risk prediction models for patient populations due to the impracticality of using multiple models simultaneously and the lack of automated systems that can analyze patient characteristics to suggest an effective model combination.
Innovation Solution
A system that automatically selects an optimal model combination by comparing combinations of risk models to target patient group characteristics, using a processor to filter, generate, and rank models based on cost functions that consider patient type, input availability, and predicted risk distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple risk prediction models are used simultaneously to improve prediction accuracy, then the reliability of patient risk assessment is improved, but the device complexity and ease of operation deteriorate due to the impracticality of monitoring all models
Solution Approach 1:
The system extracts and evaluates individual model characteristics (performance metrics, data requirements, computational demands) from the full set of available models, then selects only the necessary subset for each specific patient population. This extraction approach allows comprehensive model evaluation without requiring simultaneous monitoring of all models, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The system changes the selection criteria parameters dynamically based on patient population characteristics (e.g., disease prevalence, demographic factors, available data types). By adjusting which models are selected based on population-specific parameters, the system achieves high reliability for each population while keeping the active model set manageable in size.
2Adaptability or versatility
If iterative experimentation with different models is performed to find effective models for each environment, then the adaptability to different patient populations is improved, but the loss of time increases due to the time-consuming nature of monitoring patient events through time
Solution Approach 1:
The system performs preliminary evaluation of model characteristics and performance against population characteristics before actual deployment. By pre-assessing model suitability based on population attributes and model metadata, the system avoids time-consuming iterative experimentation during clinical implementation, achieving both adaptability and time efficiency.
Solution Approach 2:
The system introduces an intermediary automated selection process that mediates between the large set of available models and the specific patient population requirements. This intermediary system uses algorithmic matching based on population characteristics and model performance data to rapidly identify suitable models without requiring direct iterative testing by clinicians.
3Productivity
If automated analysis of patient characteristics is implemented to suggest optimal model combinations, then the productivity of model selection is improved, but the device complexity increases due to the need for automated comparison and selection systems
Solution Approach 1:
The automated selection system is segmented into distinct functional modules: population characteristic analysis, model characteristic evaluation, compatibility matching, and recommendation generation. Each module handles a specific aspect of the selection process independently, improving productivity while managing complexity through modular design that allows independent development and maintenance of each component.
Data Source
AI summary
An automated system that selects an optimal combination of risk models for a target patient population. The selected combination may be monitored by clinicians to determine which patients are at greatest risk for adverse events or clinical deterioration. The system may compare risk model data for hundreds or thousands of models to data collected on a target patient population to determine which combination of models is the best fit for this target group. An illustrative selection method may minimize a cost function that measures the deviation between a model combination and desired features for an optimal combination. Illustrative factors in the cost function may include differences between the predicted risk distributions for the target group, using the model risk function, and the risk distributions for the dataset used to train the model, and correlation among risks predicted by the models in the combination.