Correlated Prediction for Missing Data Classification
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Solution Overview
Problem
Existing multi-label classification solutions face efficiency and reliability challenges due to the need for individualized prediction techniques for each disease category, leading to high processing and memory requirements.
Innovation Solution
The method generates a singular correlated prediction by transforming a plurality of non-correlated predictions into correlated simulation data records, using a correlation matrix and simulation matrix to improve accuracy and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individualized prediction techniques are used for each disease category, then prediction accuracy for each code is improved, but processing resources and memory requirements increase exponentially
Solution Approach 1:
The patent segments the multi-label classification problem into two distinct stages: (1) generating individual non-correlated predictions for each disease category using separate prediction techniques, and (2) correlating these predictions through a correlation matrix to produce final multi-label predictions. This segmentation allows each category to be processed independently with optimized techniques while reducing overall computational complexity compared to processing all categories simultaneously.
Solution Approach 2:
The patent introduces a correlation matrix as an intermediary component that correlates the individual non-correlated predictions from multiple disease categories. This correlation matrix serves as a mediator that integrates information across categories without requiring exponential processing resources, enabling efficient combination of predictions while maintaining accuracy.
2Reliability
If traditional multi-label classification techniques are used to analyze each code, then comprehensive disease documentation is achieved, but computing resources are consumed excessively
Solution Approach 1:
The patent performs preliminary actions by generating individual non-correlated predictions for each disease category before combining them. These preliminary predictions are created using optimized single-label techniques, and then the correlation matrix integrates them efficiently. This preliminary segmentation avoids the exponential resource consumption of traditional simultaneous multi-label classification while maintaining comprehensive documentation accuracy.
3Loss of information
If multiple individualized prediction techniques are applied simultaneously, then complete disease profile documentation is achieved, but processing time increases significantly
Solution Approach 1:
The patent segments the prediction process into independent single-label classification tasks for each disease category, followed by correlation integration. This segmentation allows parallel processing of individual categories without the computational burden of simultaneous multi-label analysis, significantly reducing processing time while maintaining complete disease profile documentation through the correlation step.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a correlated prediction for an input data record by generating a correlation matrix based on co-occurrences associated with a plurality of reference non-correlated predictions, generating a simulation matrix comprising a plurality of simulation data records based on a number of simulation instances and the plurality of reference non-correlated predictions, generating a plurality of correlated simulation data records based on the correlation matrix and select ones of the plurality of simulation data records, generating one or more univariates based on the plurality of correlated simulation data records, and determining a correlated prediction based on a comparison of the one or more univariates and a plurality of input non-correlated probabilities associated with the input data record.