Modular Therapeutic Prediction Models for Immunotherapy Outcomes
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Solution Overview
Problem
Immunotherapy treatments for cancer can cause unpredictable and varying side effects such as hepatitis, pneumonitis, and colitis due to an overstimulated or misdirected immune response, making patient evaluation and treatment planning challenging.
Innovation Solution
Development of machine learning models, including toxicity-related and efficacy-related models, to predict immunotherapy side effects and efficacy using patient health data from EHRs, employing techniques like deep learning and feature engineering to generate predictions for immunotherapy outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are developed to predict immunotherapy outcomes, then prediction accuracy improves, but model complexity increases
Solution Approach 1:
The patent divides the prediction task into separate specialized models: a toxicity prediction model and an efficacy prediction model. Each model focuses on a specific aspect of immunotherapy outcomes, allowing for more accurate predictions while maintaining manageable complexity through modular architecture. The toxicity model processes features related to adverse events, while the efficacy model processes features related to treatment response.
Solution Approach 2:
The patent introduces an intermediary feature engineering layer that transforms raw patient data into standardized features suitable for model input. This intermediary processing layer includes normalization, feature selection, and transformation operations that bridge the gap between complex raw data and model requirements, improving prediction accuracy without directly increasing model complexity.
2Reliability
If multiple models are trained for different prediction tasks, then prediction reliability improves, but computational resources required increase
Solution Approach 1:
The patent segments the prediction system into distinct models with specialized training data and objectives. The toxicity model is trained on features and outcomes related to adverse events, while the efficacy model is trained on features and outcomes related to treatment response. This segmentation allows each model to be optimized for its specific task, improving overall reliability while enabling more efficient computational resource allocation compared to a single monolithic model.
Solution Approach 2:
The patent performs preliminary feature engineering and data preparation before model training. By pre-processing and transforming raw patient data into standardized features in advance, the system reduces computational burden during the actual training and prediction phases. This preliminary action includes creating feature matrices, normalizing data, and preparing training datasets, which streamlines subsequent model operations.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for generation and application of models for therapeutic prediction and processing. A balance of precision and recall can be applied to at least one of a toxicity-related model or an efficacy-related model to configure immunotherapy treatment of a patient and/or cohort of patients. Model output can be evaluated differently depending on a determine patient selection criterion to trigger different actions.