Examination Report Correction via Semantic Discrepancy Detection
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Solution Overview
Problem
Current healthcare reporting systems fail to address complex semantic and linguistic challenges in examination reports, leading to errors and inconsistencies due to ambiguous references and opaque language, which conventional spelling and grammar correction tools cannot resolve.
Innovation Solution
A method and system that extract examination data and semantic data from reports, identify discrepancies using ontologies and neural network machine learning models, and provide user feedback or autonomous resolution strategies to correct these discrepancies, leveraging natural language processing and deep learning for semantic understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spelling and grammar correction tools are used, then basic language errors can be corrected, but complex semantic and linguistic challenges cannot be addressed
Solution Approach 1:
The patent introduces an intermediary layer between conventional correction tools and examination reports. This intermediary consists of extraction modules that separate examination data and semantic data, and a discrepancy identification module that bridges them using ontologies and neural networks to detect semantic errors that conventional tools miss.
Solution Approach 2:
The patent replaces the mechanical rule-based correction system with an intelligent system using neural network machine learning models and ontologies. This substitution enables the system to understand semantic meanings and linguistic structures rather than just applying fixed grammar rules.
2Productivity
If examination reports are generated quickly using reporting software, then productivity increases, but semantic errors and inconsistencies increase
Solution Approach 1:
The patent applies preliminary action by extracting and validating examination data and semantic data before final report generation. The system proactively identifies discrepancies using ontologies and neural networks, allowing corrections to be made before the report is finalized, thus maintaining both speed and accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors extracted examination data and semantic data for inconsistencies. When discrepancies are detected, the system provides feedback for correction, creating a closed-loop process that improves report accuracy without significantly impacting generation speed.
3Measurement precision
If comprehensive semantic analysis is performed on examination reports, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex semantic analysis task into distinct modules: an extraction module that separates examination data and semantic data, a discrepancy identification module that uses ontologies and neural networks, and a resolution module. This segmentation reduces overall system complexity by making each component specialized and manageable.
Data Source
AI summary
Methods and systems for correcting an examination report. The methods described herein extract examination and semantic data from an examination report, and identify any discrepancies between the extracted examination data and the extracted semantic data. The methods described herein then receive a resolution strategy regarding how to resolve any identified discrepancies and then resolve any identified discrepancies based on the resolution strategy.