Machine Learning Models for Clinical Response Prediction
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Solution Overview
Problem
Current methods for determining clinical responses to cell therapies, such as those involving chimeric antigen receptors (CARs), lack effective approaches to predict individual patient responses, leading to challenges in optimizing treatment outcomes and dosing.
Innovation Solution
The development of methods using machine learning models, specifically random forests and random survival forests, to identify informative features associated with clinical responses by preprocessing subject, input composition, and therapeutic cell composition features, allowing for prediction of clinical outcomes before treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical response determination methods are used, then treatment protocols can be established, but individual patient response prediction remains ineffective
Solution Approach 1:
The patent segments the clinical response determination into multiple independent feature categories: subject features (demographics, clinical attributes), input composition features (cell characteristics), and therapeutic cell composition features (post-treatment measurements). This segmentation allows each feature type to be analyzed and processed independently, improving prediction precision while managing complexity through modular analysis.
Solution Approach 2:
The patent applies preliminary action by collecting and preprocessing all relevant features before treatment administration. Subject features, input composition features, and therapeutic cell composition features are gathered and processed in advance, allowing the machine learning model to make predictions prior to treatment, thereby enabling proactive treatment optimization rather than reactive adjustments.
2Measurement precision
If comprehensive feature analysis is performed to improve prediction accuracy, then clinical response can be better predicted, but data processing complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the complex multi-source feature data and the clinical response prediction. The models (including random forests, neural networks, and other algorithms) serve as mediators that automatically process, integrate, and analyze the diverse feature sets, transforming raw data into actionable predictions without requiring manual interpretation of each feature's contribution.
Solution Approach 2:
The patent applies parameter changes by transforming raw feature data into standardized formats suitable for machine learning analysis. This includes normalizing numerical values, encoding categorical variables, and adjusting feature scales to optimize model performance. The preprocessing pipeline modifies data parameters to enhance prediction accuracy while reducing processing difficulty through automated transformation.
3Adaptability or versatility
If machine learning models are implemented to predict clinical responses, then personalized treatment can be achieved, but computational resources and model training time increase
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using historical clinical data before actual patient treatment decisions are made. The models are pre-trained on comprehensive datasets containing subject features, input composition features, and therapeutic cell composition features from multiple patients, enabling rapid inference and prediction when new patients are evaluated, thus minimizing real-time computational delays.
Solution Approach 2:
The patent implements partial action by selecting and prioritizing the most influential features for model training rather than processing all possible features equally. Through feature importance analysis and selection, the system focuses computational resources on the subset of features that contribute most to prediction accuracy, reducing training time while maintaining personalized treatment capability.
Data Source
AI summary
The present disclosure relates to methods for identifying features, such as attributes of subjects, therapeutic cell compositions, and input compositions used to produce therapeutic cell compositions, associated with clinical responses of subjects, e.g., patients, following treatment with the therapeutic cell composition in connection with a cell therapy. The cells of the therapeutic cell composition express recombinant receptors such as chimeric receptors, e.g., chimeric antigen receptors (CARs) or other transgenic receptors such as T cell receptors (TCRs). The methods provide for the identification of features associated with clinical responses. In some embodiments, the methods can be used to determine (e.g., predict) a subject's response to treatment with the therapeutic cell composition.