Semantic Feature Extraction for Predictive NLP
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Solution Overview
Problem
Natural language processing systems face inefficiencies and inaccuracies in feature extraction from medical notes due to lexical diversity, terminological variety, and inefficient processing of large datasets, leading to over-extraction of irrelevant features and reduced predictive accuracy.
Innovation Solution
The system employs semantic feature extraction by adjusting occurrence frequencies of terms based on semantic dependencies, using indexed representations and hashed data structures to improve efficiency and accuracy, focusing on semantically-adjusted frequencies and limited vocabulary domains to enhance feature extraction and storage/retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional natural language processing is used to extract features from medical notes, then comprehensive feature extraction is achieved, but processing efficiency decreases and computational costs increase
Solution Approach 1:
The patent extracts only the most relevant and informative features from medical notes using semantic feature extraction. Instead of processing all possible features, the system identifies and extracts only those features that have high predictive value for clinical outcomes, thereby improving processing efficiency while maintaining feature extraction quality.
Solution Approach 2:
The patent applies different processing strategies to different parts of the medical notes based on their semantic importance. High-value terms and phrases receive more detailed processing, while less important content is processed more efficiently or skipped, optimizing the balance between completeness and efficiency.
2Measurement precision
If semantic feature extraction with adjusted frequencies is implemented, then predictive accuracy improves, but system complexity increases
Solution Approach 1:
The patent adjusts the frequency parameters of terms based on their semantic relationships and predictive importance. By dynamically changing these frequency parameters rather than using simple occurrence counts, the system achieves higher predictive accuracy. The adjustment considers whether terms appear in contexts that increase or decrease their predictive value.
Solution Approach 2:
The patent performs preliminary semantic analysis and term frequency adjustment before the main predictive modeling step. By pre-processing the data to optimize term frequencies based on semantic relationships, the system reduces the complexity of subsequent modeling while improving accuracy.
3Device complexity
If all terms in medical notes are processed with equal frequency, then simple processing is maintained, but conceptually significant features are undetected
Solution Approach 1:
The patent applies different frequency weighting to different terms based on their semantic importance and contextual relevance. Instead of uniform processing, the system identifies conceptually significant terms and assigns them higher weights, while less important terms receive lower weights or are excluded, thereby improving feature detection accuracy without requiring complex processing of all terms equally.
Data Source
AI summary
There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.