Risk Proposal System Using Controllable Feature Analysis
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Solution Overview
Problem
Existing risk prediction systems fail to provide effective proposals for reducing risks beyond weight management, as they do not account for multiple controllable features simultaneously, limiting their ability to offer comprehensive risk reduction strategies.
Innovation Solution
A proposal system that utilizes a prediction model to identify controllable features and proposes value changes based on statistical analysis of related case data, incorporating time constraints and domain knowledge to create implementable risk reduction targets, displayed through a user interface for healthcare professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a prediction model is used to predict future risks based on entity data, then the ability to predict risks is improved, but the system cannot provide effective proposals for reducing risks when multiple controllable features are involved
Solution Approach 1:
The system segments the analysis by identifying individual controllable features from entity data and analyzing each feature's contribution to risk separately. The proposal creation process breaks down the complex multi-feature problem into manageable individual feature proposals, each evaluated for its impact on risk reduction.
Solution Approach 2:
The system adds a new dimension to risk prediction by not only predicting future risk levels but also providing actionable proposal information that indicates how risk can be reduced through feature value changes. This transforms the system from a passive prediction tool to an active guidance tool.
2Adaptability or versatility
If proposals are provided for changing feature values to reduce risk, then the comprehensiveness of risk reduction strategies is improved, but the complexity of processing multiple features increases
Solution Approach 1:
The system performs preliminary analysis by identifying controllable features and their current values before generating proposals. It pre-processes entity data to determine which features can be modified and what their potential impact on risk would be, thereby simplifying the subsequent proposal generation process.
Solution Approach 2:
The system focuses on changing specific parameters (feature values) of controllable features to achieve risk reduction. By identifying key parameters that, when modified, lead to significant risk reduction, the system manages complexity while providing comprehensive strategies.
3Reliability
If statistical analysis of related case data is used to create feature value change proposals, then the implementability of risk reduction is improved, but the data processing requirements increase
Solution Approach 1:
The system extracts only the necessary information from related case data - specifically focusing on feature values and their relationship to risk outcomes. Rather than processing entire datasets, it extracts relevant feature-value pairs that demonstrate successful risk reduction patterns.
Solution Approach 2:
The system creates proposals by copying successful patterns from related cases - identifying feature value changes in historical data that led to risk reduction and recommending similar changes for current entities. This leverages proven patterns without requiring complex original analysis.
Data Source
AI summary
A system is configured to acquire, from history data, entity data similar to target entity data which is data including a feature value of each of plurality of features related to a target entity. The history data includes entity data for each of a plurality of entities at each past time point. For each entity, entity data at a past time point includes a feature value at the time point for each feature of the entity. The system is configured to create, for each controllable feature among the plurality of features related to the target entity, based on statistics of a plurality of feature values in related case data which is data including entity data similar to the target entity data, a proposal of a feature value change for reducing a risk predicted by inputting the target entity data to a prediction model. A user interface which displays the proposal.