Health State Transition Estimation Using Simultaneous Distributions
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Solution Overview
Problem
Existing technologies struggle to estimate state transitions in healthcare data without relying on temporal data from the same target, limiting their ability to predict future states accurately.
Innovation Solution
An estimation device that calculates state transition probabilities using accumulated health data across different stages, employing algorithms like optimal transport to estimate simultaneous distributions and transition probabilities without requiring temporal data from the same individual.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal data from the same target is used for state transition estimation, then prediction accuracy is improved, but data availability and ease of data collection deteriorate
Solution Approach 1:
The patent segments the estimation process into multiple stages (first stage, second stage, third stage) and estimates simultaneous distributions at each stage. This allows the system to build predictions incrementally using cross-sectional data from different stages rather than requiring complete temporal data from the same target, thereby maintaining prediction accuracy while improving data availability.
Solution Approach 2:
The patent introduces simultaneous distributions as an intermediary mechanism to connect data from different stages. By estimating simultaneous distributions that represent the relationship between variables at each stage, the system can infer state transitions without directly observing the same target over time, thus bridging the gap between cross-sectional data and longitudinal prediction.
2Measurement precision
If multiple stages of data are processed to estimate simultaneous distributions, then state transition estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex task of state transition estimation into manageable segments by processing data stage-by-stage. Each stage involves estimating a simultaneous distribution independently, which breaks down the overall computational burden while maintaining the ability to capture multi-stage transitions, thus improving accuracy without overwhelming computational complexity.
Solution Approach 2:
The patent performs preliminary estimation of simultaneous distributions at each stage before conducting the final state transition estimation. This preliminary action organizes the data and relationships in advance, making the subsequent computation more efficient and manageable, thereby reducing overall computational complexity while preserving estimation accuracy.
Data Source
AI summary
An estimation device includes a reception unit that receives an input of a data set for each stage, a first estimation unit that estimates a first simultaneous distribution based on a transition from a data distribution in a first stage to a data distribution in a second stage that is a stage after the first stage, a second estimation unit that estimates a second simultaneous distribution based on a transition from the estimated first simultaneous distribution to a data distribution in a third stage that is a stage after the second stage, and a calculation unit that calculates a state transition probability related to data transition from the second stage to the third stage based on the second simultaneous distribution. The estimation device can support decision making regarding future states by predicting future states through the estimation of state transitions.