Transductive Prognostic Model Optimization for Personalized Medicine
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Solution Overview
Problem
Current data analysis methods for personalized medicine lack the ability to create highly accurate and informative models that account for individual variability, leading to ineffective treatments for a significant portion of patients, particularly in aggressive diseases like cancer.
Innovation Solution
A computer-implemented method that optimizes the selection of features and nearest neighbors to create personalized prognostic models by determining the most relevant variables and samples for each individual, using techniques such as genetic algorithms and weighted distance measures to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If global models are used for disease prediction and treatment, then the system can be applied to the entire population with a single model, but the treatment effectiveness is limited to only about 70% of patients due to individual variability
Solution Approach 1:
The patent divides the global population into multiple local clusters or neighborhoods based on similarity in feature space. Each cluster has its own specialized model trained on locally relevant data, allowing the system to adapt to individual variability while maintaining broad population coverage. This segmentation resolves the contradiction by enabling both population-wide applicability and personalized effectiveness.
Solution Approach 2:
The patent implements local learning models that are specialized for specific local regions in the feature space rather than using a single global model. Each local model is trained on data from its specific neighborhood, giving it local quality and expertise. This allows the system to maintain high reliability for individual patients while still covering the entire population through the collection of local models.
2Reliability
If local learning models are used to capture individual variability, then treatment effectiveness for individuals improves, but the system complexity increases due to multiple models and feature selection requirements
Solution Approach 1:
The patent employs dynamic feature selection that adapts to each local cluster's characteristics. Rather than using a fixed set of features for all models, the system dynamically identifies and selects the most relevant features for each local region. This dynamic adaptation reduces the effective complexity of each local model while maintaining high personalized accuracy, as each model only needs to consider its locally relevant features.
Solution Approach 2:
The patent changes key parameters such as the number of nearest neighbors (K) and feature subsets based on the local data characteristics. By allowing these parameters to vary across different local models rather than being fixed globally, the system achieves high personalized accuracy without requiring excessively complex model structures. Each local model is optimized with parameters suited to its specific data region.
3Measurement precision
If more features and samples are selected for each individual model, then prediction accuracy improves, but the computational time and data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and samples for each local model rather than using all available data. By selecting K nearest neighbor samples and a subset of most relevant features for each local cluster, the system achieves high prediction accuracy with reduced data volumes. This extraction approach significantly decreases computational time and processing requirements compared to using the complete dataset for each individual model.
Solution Approach 2:
The patent uses a partial approach by selecting only K nearest neighbors and a subset of features rather than processing all available data. This partial action is sufficient to achieve high local prediction accuracy while dramatically reducing computational burden. The system performs just enough data processing to capture the essential local patterns without the excessive computation that would result from using all features and samples.
Data Source
AI summary
A method, computer system, and computer memory medium optimizing a transductive model Mx suitable for use in data analysis and for determining a prognostic outcome specific to a particular subject are disclosed. The particular subject may be represented by an input vector, which includes a number of variable features in relation to a scenario of interest. Samples from a global dataset D also having the same features relating to the scenario and for which the outcome is known are determined. In an embodiment, a subset of the variable features within a neighborhood formed by the samples are ranked in order of importance to an outcome. The prognostic transductive model is then created based, at least in part, on the subset, the ranking, and the neighborhood. The subset and the neighborhood are then optimized until the accuracy of the transductive model is maximized.