Machine Learning Model for Infectious Disease Testing Protocols
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Solution Overview
Problem
Current testing platforms for infectious diseases, such as COVID-19, lack a safe and effective routine for determining appropriate testing protocols, especially in the context of emerging pandemics where asymptomatic spread and varying population immunity pose significant challenges.
Innovation Solution
A data-driven system utilizing a computing device to receive user data, generate training data, and train a machine-learning model that correlates user parameters with infectious disease prediction parameters, generating infectivity parameters and determining a confidence metric to inform testing protocols based on comparison to a retest target threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routine testing is implemented broadly, then detection coverage is improved, but resource consumption and operational complexity increase
Solution Approach 1:
The system dynamically changes testing parameters (confidence thresholds, retest intervals) based on individual user risk profiles generated by the machine learning model, allowing routine testing to be adapted to each user's specific circumstances rather than applying a uniform protocol to all users
Solution Approach 2:
The system implements feedback loops where test results and user data are continuously fed back into the machine learning model to update risk assessments and adjust future testing recommendations, enabling the system to learn from outcomes and optimize resource allocation over time
2Measurement precision
If machine-learning models are trained with extensive user data, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing user data before it enters the machine learning model, and by pre-calculating risk profiles for users with incomplete data, reducing the computational burden during actual prediction operations
Solution Approach 2:
The machine learning model is segmented into modular components that can process different aspects of user data independently, allowing for parallel processing and reducing overall computation time while maintaining prediction accuracy
Data Source
AI summary
A system for a data driven disease test result prediction, the system comprising a computing device configured to receive user data, wherein the user data includes user parameters, generate, using the user data, training data wherein the training data includes a plurality of entries wherein each entry correlates user parameter data to at least a prediction parameter of the plurality of prediction parameters associated with an infectious disease, train, using the training data and a machine-learning process, a machine-learning model, wherein the trained machine-learning model is configured to generate a plurality of infectivity parameters; compare the plurality of infectivity parameters to a retest target threshold, and determine, as a function of the comparison, a confidence metric, wherein the confidence metric informs a testing protocol.