Multi-paradigm Feature Representation for Prediction Accuracy
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Solution Overview
Problem
Existing methods for transforming raw data into feature representations for prediction models in healthcare fraud detection and disease progression analysis often result in information loss and inaccurate outputs due to incomplete capture of intrinsic data properties.
Innovation Solution
A system that generates multi-paradigm feature representations by using a combination of semantic and structural machine learning models to process code description and relation metadata, merging semantic feature vectors and structural feature vectors to create comprehensive feature vectors for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data transformation methods are used to convert raw data into feature representations, then the transformation process is simple and fast, but information is lost and intrinsic data properties are not fully captured resulting in inaccurate prediction outputs
Solution Approach 1:
The patent segments the transformation process into multiple specialized components: semantic analysis module that processes textual descriptions to extract meaning, structural analysis module that processes hierarchical relationships, and relation analysis module that processes association metadata. Each module captures specific aspects of the raw data independently, preventing information loss that would occur in a single transformation step.
Solution Approach 2:
The patent creates a composite feature representation by combining multiple types of processed information: semantic features from textual descriptions, structural features from hierarchical relationships, and relation features from association metadata. This composite approach ensures that diverse intrinsic properties of the raw data are all captured and preserved in the final feature representation.
2Loss of information
If multiple types of metadata are processed to capture all intrinsic properties, then information completeness is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent divides the complex transformation task into separate modular modules: a semantic analysis module for textual descriptions, a structural analysis module for hierarchical relationships, and a relation analysis module for association metadata. Each module handles a specific type of metadata independently, making the overall system more manageable and maintainable despite processing multiple metadata types.
Solution Approach 2:
The patent employs machine learning models that serve multiple functions: they process different types of metadata (textual, hierarchical, relational) and generate corresponding feature vectors that are then integrated. This multi-functionality reduces the need for separate specialized components for each metadata type, thereby controlling system complexity while maintaining information completeness.
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating multi-paradigm feature representations are provided. An example method may include generating a code dataset including a plurality of codes associated with a predictive entity; generating a plurality of semantic feature vectors based at least in part on code description metadata; generating a plurality of structural feature vectors based at least in part on code relation metadata; generating a plurality of multi-paradigm feature vectors based at least in part on the plurality of semantic feature vectors and the plurality of structural feature vectors; generating a prediction for the predictive entity by processing the plurality of multi-paradigm feature vectors using a prediction model; and performing one or more prediction-based actions based on the prediction.