Classification Code Parser for Medical Notes
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Solution Overview
Problem
Current automated solutions fail to accurately and efficiently assign classification codes to medical documents, particularly those with grammatical errors, shorthand, or technical terms, as existing natural language processing (NLP) technologies are limited by the need for correct grammar and standard vocabulary, and require large datasets for training, which are often unavailable in many industries.
Innovation Solution
A classification code parser that reads and tokenizes medical notes, creates a keyword map, determines match ratios and proximity factors, and calculates the strength of matches between note tokens and classification codes, enabling automatic identification and association of classification codes with written notes and documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NLP technology is used to parse medical notes, then grammatically correct text with standard vocabulary can be processed, but notes with grammatical errors, shorthand, or technical terms cannot be parsed accurately
Solution Approach 1:
The patent changes the fundamental parameters of text processing by abandoning grammar-based parsing in favor of token-based pattern matching. Instead of requiring correct grammatical structure, the system tokenizes text and matches classification codes based on keyword presence and positional relationships, enabling reliable parsing of non-standard medical notes while maintaining adaptability to various text formats
Solution Approach 2:
The patent substitutes the mechanical grammar-checking system of traditional NLP with a keyword-matching system that uses tokenization and positional analysis. This replacement allows the system to process text without enforcing grammatical rules, thereby achieving both high reliability in parsing accuracy and versatility in handling diverse text formats including shorthand and technical terms
2Measurement precision
If statistical NLP or machine learning systems are trained with large datasets, then classification accuracy may improve, but the requirement for extensive training data becomes a barrier when data is unavailable
Solution Approach 1:
The patent enables the system to perform classification without external training data by using self-contained keyword matching rules and positional analysis. The system serves itself by incorporating classification logic directly into the parsing algorithm, eliminating the need for separate training phases and large datasets while maintaining classification accuracy through rule-based pattern recognition
Solution Approach 2:
The patent extracts the essential classification information directly from the text through tokenization and keyword matching, removing the dependency on training data. By extracting classification codes based on presence and positional relationships of keywords rather than statistical patterns, the system achieves accurate classification without requiring extensive training datasets
3Reliability
If manual classification code assignment is performed by medical professionals, then accurate codes can be assigned based on understanding of medical facts, but the process becomes impractical and impossible for massive amounts of documents
Solution Approach 1:
The patent introduces an intermediary automated parsing system that bridges the gap between unstructured medical notes and classification codes. This intermediary system uses tokenization and keyword matching to automatically assign codes with accuracy comparable to manual assignment, thereby maintaining reliability while dramatically increasing productivity by processing massive amounts of documents that would be impossible to classify manually
4Productivity
If automated classification systems are designed to process text quickly, then millions of notes can be classified in hours, but the system must handle diverse text formats including shorthand and technical terms
Solution Approach 1:
The patent segments text into tokens and processes them individually based on keyword presence and positional relationships rather than analyzing complete grammatical structures. This segmentation approach simplifies the processing logic while maintaining the ability to handle diverse text formats, thereby achieving high classification speed without excessive system complexity
Data Source
AI summary
A classification code parser and method can include: reading a classification code having a description; reading a required keyword, and a total number of keywords associated with the classification code; reading text of a note; tokenizing the text of the note to create a note token stream, the note token stream having a note token and a position of the note token within the note token stream; creating a keyword map including a total number of matched keywords; determining a match ratio from the total number of the matched keywords and the total number of the keywords; determining a proximity factor based on a shortest span of tokens within the note token stream containing all the matched keywords; and determining a strength of a match between the classification code and the note based on the match ratio being multiplied by the proximity factor.