Subject Resource Matching via Segmented Classification
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Solution Overview
Problem
Existing resource allocation methods struggle to optimize resource utilization and workflow for non-average subjects, as they are often based on average or typical subject profiles, leading to suboptimal resource allocation and inefficient workflow in medical and clinical settings.
Innovation Solution
A method that involves obtaining data for a subject, generating data groups for specific subsets of parameters, and applying classification processes to match the subject to resources or workflow steps, using rule-based or machine learning algorithms depending on data sparsity, to ensure tailored resource allocation and efficient workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is split into multiple groups for classification, then classification reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the classification process into multiple independent data groups, each processed by separate classification algorithms. This segmentation allows each classifier to focus on specific parameter subsets, improving overall classification reliability while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent transforms the classification problem from a single high-dimensional space into multiple lower-dimensional subspaces by grouping parameters. This dimensional transformation improves classification reliability by reducing the complexity of each individual classification task while collectively covering the full parameter space
2Reliability
If conventional clustering is applied to all parameters, then comprehensive analysis is achieved, but data sparsity reduces classification reliability
Solution Approach 1:
By segmenting the parameter space into multiple groups, the patent ensures that each group contains sufficient data samples for reliable classification. This prevents data sparsity issues that would occur if all parameters were analyzed together in a single high-dimensional space
Solution Approach 2:
The patent applies classification to subsets of parameters rather than attempting to classify all parameters simultaneously. This partial action approach ensures adequate data population in each subset, improving classification reliability even though not all parameters are processed with the same depth
Data Source
AI summary
Presented are concepts for matching a subject to one or more resources or workflow steps. Once such concept comprises obtaining data associated with a subject, the data comprising, for each of a plurality of parameters, a parameter value relating to the subject. A plurality of data groups for characterising the subject is then generated and a classification process is applied to each data group so as to generate a classification result for each data group. The subject is then matched to one or more resources or workflow steps based on the classification results.