BPR Algorithm Dimensionality Reduction for Alzheimer's Phenotyping
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Solution Overview
Problem
Current methods for diagnosing and managing Alzheimer's disease face inaccuracies due to heterogeneity in clinical phenotypes and biological variability, particularly in FDG-PET and PiB scans, which complicates understanding individual differences and disease processes.
Innovation Solution
A novel unsupervised machine learning algorithm, Between-subject Variance Projection and Reduction (BPR), is used to determine principal components in multivariate medical data, allowing for dimensionality reduction and characterization of individuals and disease processes, enabling individualized clinical counseling and improved clinical trial designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If dimensionality reduction is applied to high-dimensional medical data, then data complexity is reduced and processing efficiency is improved, but information loss may occur
Solution Approach 1:
The patent transforms the high-dimensional medical data by changing its parameter representation through principal component analysis. The original high-dimensional parameters (voxel intensities, clinical scores) are transformed into a lower-dimensional parameter space (principal components) that captures the essential variability while reducing complexity and minimizing information loss.
Solution Approach 2:
The patent applies dimensionality reduction techniques that project data from high-dimensional space into lower-dimensional space by introducing new dimensional representations (principal components). This allows the data to be represented in fewer dimensions while preserving the essential structure and relationships among observations.
2Reliability
If unsupervised machine learning algorithms are used to learn features from large amounts of data, then previously unseen features can be discovered for characterizing individuals and disease processes, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary dimensionality reduction and feature extraction on the training data before applying the unsupervised learning algorithm. By pre-processing the data to extract principal components and reduce dimensionality, the algorithm can more efficiently learn from the reduced feature set, decreasing computational complexity and processing time while maintaining the ability to discover novel phenotypic features.
3Productivity
If principal component analysis is used to reduce high-dimensional space to compact representational space, then between-subject variability is captured efficiently, but the interpretability of individual variables may be reduced
Solution Approach 1:
The patent incorporates feedback mechanisms that allow clinicians to provide input on the phenotypic classifications generated by the algorithm. This feedback is used to refine and validate the principal components, ensuring they capture meaningful biological variability. The iterative refinement process helps maintain interpretability by aligning the mathematical components with clinically recognizable patterns.
Solution Approach 2:
The patent introduces principal components as intermediary variables that bridge the gap between high-dimensional raw data and clinically interpretable phenotypes. These components serve as mediators that capture complex patterns in the data while remaining connected to underlying biological processes, allowing both efficient computation and clinical interpretability.
Data Source
AI summary
An apparatus and computerized method for determining a set of principal components in a multivariate medical data corresponding to a group of subjects comprises: providing a computing device comprising an input/output interface, a memory and one or more processors communicably coupled to the input/output interface and the memory; receiving the multivariate medical data via the input/output interface or the memory; identifying a set of variables based on metabolic patterns between the subjects in the multivariate data using the one or more processors; representing the multivariate medical data corresponding to the set of variables in a high dimensional space between the subjects using the one or more processors; determining the set of principal components by reducing the high dimensional space between the subjects to a compact representational space using the one or more processors; and providing the set of principal components via the input/output interfaces.