NLP System for Electronic Record Categorization
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Solution Overview
Problem
Current natural language processing technologies face challenges in effectively categorizing subjects based on natural language notes from electronic medical records into clinically accepted behavioral categories, lacking the ability to utilize unstructured data for objective diagnostic assessments and monitoring.
Innovation Solution
A computer system that accesses electronic records, identifies tokens, generates intensity scores, rescales them, and categorizes subjects into pre-existing behavioral categories using natural language analysis, enabling the use of unstructured notes for clinically accepted classifications and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language processing is applied to electronic medical records, then objective diagnostic assessments and monitoring can be enabled, but the complexity of the system increases
Solution Approach 1:
The system segments the natural language processing task into distinct modules: token identification, intensity score generation, rescaling, and category assignment. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving objective diagnostic assessments through automated processing
Solution Approach 2:
The patent introduces intermediary components such as intensity scores and rescaling mechanisms that bridge the gap between raw natural language data and final diagnostic categories. These intermediaries transform unstructured notes into quantifiable metrics that can be objectively measured and compared across patients
2Adaptability or versatility
If unstructured natural language notes are processed, then previously unrealized uses of digital data can be achieved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system replaces manual clinical assessment with automated computational processes. Natural language notes are processed through algorithmic token identification and intensity scoring, substituting human subjectivity with objective computational measurement while maintaining adaptability to various clinical scenarios
Solution Approach 2:
The patent transforms qualitative natural language descriptions into quantitative parameters through intensity scoring. By converting textual expressions of symptoms into numerical intensity values on standardized scales, the system enables objective measurement and comparison while maintaining versatility across different diagnostic categories
3Productivity
If automated categorization is implemented, then productivity increases, but the precision of clinical judgment may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the automated categorization results can be reviewed and adjusted by clinicians. The intensity scores and category assignments provide structured information that guides clinical judgment, enabling high-speed processing while maintaining diagnostic precision through human-in-the-loop validation
Data Source
AI summary
Electronic records are accessed from computer storage for a given subject, wherein the electronic records include natural language notes about the subject. Tokens are identified in the natural language notes. For each token, a corresponding intensity score is generated representing an intensity of match between the token and a particular dimension, wherein the intensity scores are each values on a first scale, wherein each dimension is one of a plurality of dimensions of a category out of a plurality of categories; generating rescaled-intensity scores from the intensity scores by rescaling the intensity scores from the first scale to a second scale different from the first scale. For each dimension of each category, a dimension-score is compiled based on the intensity scores; and categorizing the subject into at least one category based on the dimension scores. The subject is categorized into at least one category based on the dimension scores.