Graph Wavelet Data Augmentation for Clinical Trial Patient Selection
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Solution Overview
Problem
In clinical studies, especially for Alzheimer's disease, it is challenging to determine which patients are likely to develop the condition in advance, making it difficult to select appropriate participants for treatment studies, as high-cost data collection methods are costly, time-consuming, and inconvenient, while low-cost methods are less accurate.
Innovation Solution
A computerized system uses wavelet expansions on graphs to identify proxy patients for whom additional high-cost data can be collected, allowing for the estimation of missing data in other patients, thereby optimizing data collection resources and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-cost data collection methods are used for all patients, then measurement precision is improved, but loss of energy increases due to cost, time, and patient inconvenience
Solution Approach 1:
The patient population is segmented into two groups: those who receive high-cost data collection and those who rely on low-cost data with statistical imputation. The system divides the data collection process into selective high-cost measurements for a subset of patients and computational estimation for the remainder, resolving the contradiction between comprehensive accuracy and resource efficiency
Solution Approach 2:
The system creates statistical copies of high-cost data for patients who did not undergo high-cost measurement. By using graph wavelet analysis and machine learning models trained on high-cost data from proxy patients, the system generates estimated high-cost data values for the broader patient population, achieving comprehensive characterization without universal high-cost measurement
2Reliability
If high-cost data is collected for all patients, then reliability of patient selection is improved, but productivity decreases due to time and resource constraints
Solution Approach 1:
The system performs preliminary low-cost data collection and graph wavelet analysis on all patients before determining who needs high-cost measurement. By pre-identifying proxy patients and using their high-cost data to create predictive models, the system enables rapid screening of large patient populations while maintaining reliable disease likelihood prediction through statistical imputation for non-proxy patients
3Loss of energy
If selective high-cost data collection is used, then loss of energy is reduced, but measurement precision deteriorates for patients without high-cost data
Solution Approach 1:
The system introduces graph wavelet analysis and machine learning models as intermediary processes between low-cost data and high-cost data estimation. These computational intermediaries translate readily available low-cost data into accurate predictions of high-cost measurements by learning complex non-linear relationships from training data, maintaining measurement precision while avoiding universal high-cost measurement
Solution Approach 2:
The system replaces the mechanical process of universal high-cost measurement with a computational system that uses graph wavelet analysis and supervised learning. Instead of physically measuring every patient with expensive equipment, the system uses algorithms to estimate high-cost data values, achieving comparable accuracy with minimal resource expenditure
Data Source
AI summary
A method of improving data sets, for example, of patients, each being characterized by relatively low-cost medical data, identifies those patients where the acquisition of higher cost medical data would best inform an estimate of the higher cost medical data for the remaining patients. In this way scarce medical resources can be more efficiently applied in characterizing a potential patient pool, for example, for a clinical trial when resources are not available for extensive medical characterization of each trial participant.