Adaptive Clinical Assessment Scheduling via Gaussian Process Regression
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Solution Overview
Problem
Existing Ecological Momentary Assessment (EMA) approaches are inefficient as they rely on pre-selected tests not optimized to the patient's behavioral or functional level, leading to redundant data collection and an inflexible testing schedule that does not adapt to changes in the patient's condition, resulting in unnecessary burden and reduced data quality.
Innovation Solution
A method and system that determine an optimal assessment schedule using machine learning techniques, such as Gaussian Process Regression and Kullback-Leibler Divergence, to adaptively collect clinical data by identifying the most informative times and conditions for data collection, allowing for real-time modification of testing schedules based on patient-specific patterns and changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a pre-determined conservative testing schedule is used to sample as much data as the patient can sustain, then the quantity of data collected is increased, but the patient burden increases and data quality decreases
Solution Approach 1:
The testing schedule transitions from a static pre-determined plan to a dynamic adaptive schedule that automatically adjusts assessment timing and selection based on real-time analysis of patient data patterns, behavioral responses, and functional levels detected during treatment progression
Solution Approach 2:
The system implements continuous feedback loops where collected EMA data is analyzed to identify patterns and changes in patient behavior or function, which then feed back into automatic updates of the testing schedule to optimize future data collection timing and selection
2Ease of operation
If pre-selected tests are used that are not optimized to the patient's behavioral or functional level, then the testing schedule is simple to implement, but the data collection efficiency decreases and redundancy increases
Solution Approach 1:
The system dynamically changes testing parameters including assessment selection, timing, and frequency based on detected patterns in patient data, automatically optimizing which tests to administer and when without requiring manual configuration
Solution Approach 2:
The system performs preliminary analysis of collected data to identify patterns and predictive indicators of disease progression or response, using these insights to proactively determine optimal future assessment timing and selection before clinical decisions are needed
Data Source
AI summary
Methods and systems providing adaptive assessment of a physical subject to efficiently collect assessment data and modify an assessment schedule based on the analyses. The methods and systems can control the timing of each assessment in order to collect data at times and under conditions that are most informative about the physical subject. Such adaptive methods and systems significantly minimize the frequency of data collection without loss in accuracy or precision and can increase test reliability through reduction in redundancy. The ability to estimate an unknown, underlying function using a small number of free parameters that remain constant regardless of the number of data points being estimated substantially reduces the error of the function estimate. Because estimates of the measurement error are achieved with a minimum of sampled assessments, and with great accuracy, the statistical power of clinical trials, for example, can be greatly increased.