Radiology Report NLP Error Detection via Character-Level Transformations
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Solution Overview
Problem
Current methods for assessing the accuracy of radiology diagnoses are subjective, unreliable, and lack a standardized, robust, and quantitative approach, leading to inefficiencies and potential misdiagnoses.
Innovation Solution
A computer-implemented system that utilizes natural language understanding techniques to analyze radiology reports, detect errors, and generate accurate replacements using character-level optical transformation costs and frequency analysis, while also integrating neural networks for named entity recognition and graph convolutions to establish linked relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language understanding techniques with neural networks are used to analyze radiology reports, then diagnostic accuracy assessment becomes standardized and quantitative, but system complexity increases
Solution Approach 1:
The system segments the radiology report analysis into distinct functional modules: error detection module, character-level optical transformation cost analysis module, frequency analysis module, named entity recognition module, and relationship extraction module. Each module performs a specific task and processes intermediate results, making the overall complex system manageable and maintainable while achieving standardized quantitative assessment
Solution Approach 2:
The patent introduces intermediary computational structures including character-level embeddings as intermediate representations between input text and final error detection, and attention mechanisms as intermediaries that weigh the importance of different text segments. These intermediaries bridge the gap between raw radiology report text and structured diagnostic accuracy assessments
2Measurement precision
If character-level optical transformation costs and frequency analysis are used to detect errors, then error detection precision improves, but computational processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing character-level embeddings and building frequency analysis data structures before actual error detection. Character-level optical transformation costs are pre-calculated and stored, allowing the main error detection process to query these pre-computed values rather than calculating them from scratch, thus reducing real-time processing time while maintaining high precision
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting specific types of errors that are most critical for diagnostic accuracy, rather than attempting to detect all possible errors with equal intensity. The frequency analysis selectively identifies character transformations that are most likely to represent meaningful errors based on their occurrence patterns
Data Source
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AI summary
A natural language understanding method begins with a radiological report text containing clinical findings. Errors in the text are corrected by analyzing character- level optical transformation costs weighted by a frequency analysis over a corpus corresponding to the report text. For each word within the report text, a word embedding is obtained, character-level embeddings are determined, and the word and character-level embeddings are concatenated to a neural network which generates a plurality of NER tagged spans for the report text. A set of linked relationships are calculated for the NER tagged spans by generating masked text sequences based on the report text and determined pairs of potentially linked NER spans. A dense adjacency matrix is calculated based on attention weights obtained from providing the one or more masked text sequences to a Transformer deep learning network, and graph convolutions are then performed over the calculated dense adjacency matrix.