Treatment Effect Prediction Model Variance Control
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Solution Overview
Problem
Existing digital therapy (DTx) systems do not adequately consider improving the reliability of treatment effects, as slight changes in predictors can lead to varying treatment outcomes.
Innovation Solution
An information processing apparatus that constructs a treatment effect prediction model, which includes a treatment method output model, and adjusts this model by inactivating weighting factors based on tendency scores to ensure the variance of treatment effects falls within a predetermined range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the model is updated based on collected data to improve treatment effect, then the treatment effect may vary due to slight changes in the predictor, but the reliability of the treatment effect is not improved
Solution Approach 1:
The patent applies preliminary action by constructing a treatment effect prediction model before actual treatment to predict and evaluate potential treatment effects. This allows the system to pre-assess the reliability and stability of treatment effects by generating predicted values from multiple predictors, and only selecting treatment methods with predicted effects meeting predetermined thresholds, thereby preventing unreliable treatments from being applied.
Solution Approach 2:
The patent implements feedback by comparing predicted treatment effects with actual treatment outcomes and using this information to adjust and refine the treatment effect prediction model. The system collects actual treatment effect data, compares it with predicted values, and updates the model parameters to improve future predictions, creating a continuous improvement loop that enhances reliability over time.
Solution Approach 3:
The patent applies parameter changes by dynamically adjusting the weighting factors of different predictors in the treatment effect prediction model. The system changes the importance parameters of various predictors based on their performance and relevance, allowing the model to adapt to different treatment scenarios and improve the stability of treatment effect predictions by optimizing the contribution of each predictor.
2Productivity
If a treatment method with high treatment effect is output by the updated model, then the treatment effect may vary due to slight change in the predictor
Solution Approach 1:
The system performs preliminary evaluation of treatment effects by generating predicted values from multiple predictors before finalizing treatment recommendations. This preliminary action allows the system to identify treatment methods that consistently produce high effects across different predictors, filtering out treatments with high variance before they are recommended to patients.
Solution Approach 2:
The system changes the weighting parameters of predictors dynamically to optimize both treatment effect and consistency. By adjusting the importance of different predictors based on their reliability and performance metrics, the system can maintain high treatment effects while reducing variability caused by slight changes in individual predictors.
Data Source
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AI summary
Provided is an information processing apparatus capable of improving reliability of a treatment effect by an output treatment method. An information processing apparatus using a treatment method output model that outputs a treatment method according to a state of a patient includes a treatment effect prediction model construction unit that constructs a treatment effect prediction model that includes the treatment method output model as a component, and compares a treatment effect when the treatment method is used with a treatment effect when the treatment method is not used, and a model adjustment unit that generates a prediction model group by inactivating a weighting factor of the treatment effect prediction model based on a tendency score when the treatment method is used and when the treatment method is not used, and adjusts the treatment method output model that is the component of the treatment effect prediction model such that a variance of a treatment effect output from the prediction model group falls within a predetermined range.