NLP Medical Record Classification via Section Prioritization
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Solution Overview
Problem
The challenge in the medical field is accurately classifying patients from multiple medical documents to identify those meeting specific clinical criteria, due to varying data formats and inconsistent terminology, which complicates the selection of subjects for studies or trials.
Innovation Solution
The use of computer-based techniques involving natural language processing (NLP) to analyze medical documents, prioritize sections, and identify clinical concepts, including ontologies and quantitative indications, to accurately classify subjects associated with selected clinical concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer-based analysis is used to analyze multiple medical documents simultaneously, then productivity is improved, but measurement precision deteriorates due to varying data formats and inconsistent terminology
Solution Approach 1:
The patent transforms unstructured medical text into structured data by extracting specific parameters (clinical concepts, entities, relationships) and normalizing them into a standardized format. This parameter transformation enables consistent analysis across varying data formats while maintaining classification accuracy through systematic parameter extraction and normalization processes
Solution Approach 2:
The patent introduces an intermediary processing layer that includes natural language processing, entity recognition, and relationship extraction components. This intermediary layer acts as a mediator between the raw medical documents with varying formats and the analysis system, translating diverse input formats into a unified structured representation that maintains precision while enabling efficient batch processing
2Measurement precision
If manual classification of medical documents is performed to ensure accuracy, then measurement precision is improved, but productivity deteriorates due to the time-consuming nature of manual review
Solution Approach 1:
The patent implements self-service classification through automated natural language processing and machine learning algorithms that perform document analysis without human intervention. The system automatically extracts clinical concepts, identifies entities, establishes relationships, and classifies documents based on learned patterns from training data, enabling high-volume processing while maintaining consistent accuracy through algorithmic decision-making
Solution Approach 2:
The patent performs preliminary actions by pre-processing medical documents through entity recognition, relationship extraction, and structured data generation before the actual classification task. This preliminary structuring of data creates a standardized foundation that enables subsequent automated classification to achieve manual-level accuracy at machine speed, eliminating the need for time-consuming manual review
Data Source
AI summary
This disclosure includes a method of classifying a plurality of subjects associated with medical documents. The method includes receiving, with a computer system, an indication of at least one clinical concept, parsing, with the computer system, the medical documents for corresponding indications of the clinical concept, identifying, with the computer system, subjects in the plurality of subjects as meeting the clinical criterion based on prioritization of sections within the medical documents and locations of the corresponding indications of the clinical concept within the medical documents, and outputting, with the computer system, indications of the subjects in the plurality of subjects identified as meeting the clinical criterion.