Physiological Prediction Device Dynamic Range Visualization
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Solution Overview
Problem
Existing prediction devices struggle to visually represent changes in prediction accuracy of physiological information due to variations in the observation environment of the observed parameter, particularly with changing time intervals for data acquisition.
Innovation Solution
A prediction device and method that input time series data into a prediction model to forecast unobserved values and visualize a range within which these values may fall, with the range dynamically changing based on the time interval between data points, enabling users to recognize changes in forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the time interval between data points is reduced to improve prediction accuracy, then measurement precision improves, but productivity decreases due to increased data acquisition frequency requirements
Solution Approach 1:
The system dynamically adjusts the time interval between data point acquisitions based on the subject's state and prediction requirements. The time interval is not fixed but varies adaptively, allowing the system to optimize between prediction accuracy and data acquisition efficiency in different operational contexts
Solution Approach 2:
The system changes the time interval parameter dynamically based on prediction accuracy requirements and subject conditions. By adjusting this temporal parameter, the system optimizes the balance between obtaining sufficient prediction accuracy and maintaining efficient data acquisition rates
2Productivity
If the time interval between data points is increased to improve productivity, then data acquisition efficiency improves, but measurement precision deteriorates due to reduced prediction accuracy
Solution Approach 1:
The system employs dynamic time interval adjustment where the interval between data acquisitions is modified in real-time based on prediction accuracy requirements. This allows the system to maintain high productivity when high precision is not critical while ensuring prediction accuracy is sufficient for clinical decision-making
3Ease of operation
If fixed time intervals are used for data acquisition to simplify operation, then ease of operation improves, but adaptability deteriorates because the system cannot adjust to varying observation environments
Solution Approach 1:
The system performs self-adjustment of data acquisition intervals based on automated analysis of prediction accuracy requirements and subject conditions. The system monitors its own performance and autonomously modifies operational parameters without requiring manual intervention, thereby maintaining ease of operation while achieving high adaptability
Solution Approach 2:
The system implements feedback mechanisms where prediction accuracy results and subject state information are continuously monitored and used to adjust future data acquisition intervals. This closed-loop control enables the system to adapt to varying environments while maintaining simple operation through automated decision-making
Data Source
AI summary
An interface receives time series data including multiple observed values of an observed parameter that are acquired at different time points for obtaining physiological information of a subject. A processor inputs the time series data into a prediction model to perform prediction of one or more unobserved values of the observed parameter. The processor causes an output device to visualize a range within which the one or more unobserved values may fall. The range is changed in accordance with a time interval between the different time points.