Manifold Clustering for Non-Linear Patient Behavior Data
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Solution Overview
Problem
Current technologies lack effective methods to manage patient engagement for chronic diseases, particularly in non-linear data scenarios, leading to inefficient self-care plans and high healthcare costs due to misalignment of digital health services with individual motivation and behavior constructs.
Innovation Solution
A method using manifold clustering based on statistically significant association patterns to identify and customize self-care plans for patients, allowing for dynamic generation of clusters without predefinition, which enhances patient compliance and reduces resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional linear prediction techniques (linear regression, PCA) are used, then the method is simple and computationally efficient, but it fails to capture non-linear relationships in behavior constructs
Solution Approach 1:
The patent transforms the fundamental assumption of linearity into non-linearity by employing manifold clustering and information-theoretic techniques. This parameter change enables the system to capture complex non-linear relationships in patient behavior data while maintaining computational feasibility through dimensionality reduction and pattern association methods.
Solution Approach 2:
The patent replaces conventional linear statistical methods with information-theoretic based techniques and manifold learning algorithms. This substitution introduces a new computational paradigm that naturally handles non-linear relationships without requiring complex preprocessing or transformation of the underlying data structure.
2Measurement precision
If information-theoretic based techniques (ID3) are used, then non-linear relationships are captured, but the computational complexity becomes exponential with respect to the number of type enumerations
Solution Approach 1:
The patent segments the high-dimensional data space into lower-dimensional manifolds that capture the essential non-linear relationships. By dividing the complex computational problem into manageable manifold components, the system achieves exponential complexity reduction while preserving the ability to capture non-linear patterns in patient behavior data.
Solution Approach 2:
The patent projects high-dimensional data onto lower-dimensional manifolds embedded in reduced spaces. This dimensionality change transforms the computational problem from exponential complexity in the original space to polynomial complexity in the manifold space, while maintaining the ability to represent non-linear relationships through the geometric structure of the manifolds.
3Measurement precision
If spectral clustering is used for manifold clustering, then non-linear clustering is achieved, but the method is sensitive to initial seeding and requires a 2-phase approach
Solution Approach 1:
The patent performs preliminary dimensionality reduction and manifold identification before applying clustering algorithms. This preliminary action prepares the data in a form that is less sensitive to initial seeding conditions, enabling more robust and convergent clustering results while reducing the need for complex multi-phase approaches.
4Device complexity
If conventional clustering methods are used, then the implementation is straightforward, but they cannot handle mixed data types (Real and finite discrete type)
Solution Approach 1:
The patent develops a universal manifold clustering framework that can handle multiple data types (Real and finite discrete) within a unified mathematical structure. This universal approach eliminates the need for separate processing pipelines for different data types, maintaining implementation simplicity while achieving broad data type versatility through type-agnostic manifold learning techniques.
Data Source
AI summary
A method for identifying a manifold cluster using statistically significant association patterns. A data set of real, continuous numbers is received and converted to corresponding discrete data representations. Statistically significant association patterns of the data are utilized to generate a manifold cluster. Customized actions, such as customized messages, are generated that are specific to the manifold cluster.