VST Response Prediction Using Generative Clinical Data Augmentation
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Solution Overview
Problem
The challenge of accurately predicting the efficacy of virus-specific T-cell (VST) treatments for immunocompromised patients is hindered by the lack of large datasets and complex underlying relationships between clinical variables, leading to variable treatment responses and difficulty in deciding between VST therapy and antiviral therapy.
Innovation Solution
A method utilizing a neural network (NN) to analyze patient variables and generate synthetic data through a generative model like a variational autoencoder (VAE) to enhance predictive models, allowing for improved decision-making on treatment choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive models are used for VST treatment efficacy, then the model complexity remains manageable, but the predictive power and accuracy are insufficient due to lack of large datasets
Solution Approach 1:
The patent applies generative models to create synthetic copies of patient data that replicate the statistical properties and relationships of real clinical data. These synthetic datasets augment the limited real data, enabling training of more accurate predictive models without requiring actual additional patient records.
Solution Approach 2:
The patent transforms the data from real patients into synthetic data through generative models, changing the data parameters while preserving the underlying clinical relationships. This allows the model to learn from augmented data that maintains the essential patterns needed for accurate VST efficacy prediction.
2Measurement precision
If more clinical variables are included in the predictive model, then the predictive accuracy improves, but the complexity of underlying relationships becomes harder to manage
Solution Approach 1:
The generative model acts as an intermediary that processes multiple clinical variables and transforms them into synthetic data representations. This intermediary layer simplifies the complex relationships between numerous variables by capturing their joint distributions and interactions in a compressed, manageable format that the predictive model can effectively process.
3Quantity of substance
If synthetic data is generated to increase sample size, then the predictive power improves, but the complexity of the data generation process increases
Solution Approach 1:
The patent uses generative models to create synthetic copies of patient records that preserve the statistical properties and clinical relationships of real data. This copying approach increases the effective sample size while the automated generative process manages the complexity of data generation.
Data Source
AI summary
A method for treating an immunocompromised patient including collecting from patient values for at least one variable selected from the group, inputting the values for the one or more variables to a neural network (NN) performed on one or more computers to produce a score indicative of likelihood of response or non-response to an anti-viral drug and a score indicative of likelihood of response or non-response to Virus-Specific T-Cells (VSTs) and administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a response to anti-viral therapy and administering VSTs to a patient who has a threshold score indicative of a likelihood of a response to VST therapy.