Radiological Report Error Detection via NLP and Graph Convolutions
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Solution Overview
Problem
Current methods for assessing diagnostic accuracy and error rates in radiology are inadequate, as they rely on subjective expert opinions and lack a standardized, quantitative approach, leading to variability and inefficiency in evaluating diagnostic quality and accuracy across different radiology providers.
Innovation Solution
A computer-implemented system that uses natural language understanding and machine learning techniques to analyze radiological reports, including optical character recognition, named entity recognition, and graph convolutions, to identify errors and generate standardized data for assessing diagnostic accuracy and error rates, incorporating frequency analysis and word embeddings to improve interpretation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language understanding and machine learning techniques are used to analyze radiological reports, then measurement precision and reliability of diagnostic error assessment are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system segments the complex task of diagnostic error assessment into multiple specialized machine learning components: optical character recognition for text extraction, named entity recognition for identifying medical terms, graph convolutional networks for relationship modeling, and frequency analysis for error pattern detection. Each component handles a specific aspect of the analysis, improving overall measurement precision while managing system complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing layers including word embeddings that transform raw text into meaningful numerical representations, and graph structures that serve as intermediaries between unstructured radiological reports and structured error assessments. These intermediaries bridge the gap between complex input data and precise measurement outputs.
2Reliability
If standardized quantitative methods are implemented for assessing diagnostic accuracy, then reliability and objectivity of assessment are improved, but ease of operation and ease of manufacture decrease
Solution Approach 1:
The system implements self-service through automated machine learning pipelines that independently perform text extraction, entity recognition, relationship modeling, and error detection without requiring manual intervention. The frequency analysis component automatically identifies error patterns from the data, enabling the system to serve itself in maintaining standardized assessment protocols while ensuring reliability and objectivity.
3Measurement precision
If machine learning techniques including graph convolutions and word embeddings are used for error detection, then measurement precision of diagnostic errors is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing radiological reports through optical character recognition and named entity recognition before the main error detection analysis. Word embeddings are pre-computed and stored, allowing the graph convolutional networks to work with ready-to-use numerical representations rather than raw text, significantly reducing processing time while maintaining high measurement precision.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific regions of interest within the radiological reports. The graph convolutional networks concentrate analysis on identified entities and their relationships rather than processing the entire text uniformly, improving error detection accuracy in critical areas while reducing overall computational time and resource loss.
Data Source
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.