Automated Capsule Endoscopy Report Structuring via NLP Annotation
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Solution Overview
Problem
Manual annotation of capsule endoscopy reports is labor-intensive, costly, and inefficient, leading to potential omissions and inconsistencies due to varying doctor habits and heavy workload, while also occupying significant storage space with unstructured text.
Innovation Solution
A method using deep learning models like BiLSTM+CRF and BERT to automatically annotate and structure capsule endoscopy report text, employing a hierarchical tree structure and panel data to extract and store named entity classification labels and time parameters, significantly reducing manual workload and improving data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual annotation is used to organize examination reports, then the reports can be annotated and organized, but it wastes manpower and increases the cost of annotation
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated natural language processing system. The system uses named entity recognition technology to automatically identify and annotate key information in capsule endoscopy reports, eliminating the need for manual annotation by medical staff while maintaining annotation quality and consistency.
2Reliability
If manual annotation is used to organize examination reports, then the reports can be annotated, but omissions and mistakes may be caused due to heavy workload and varying doctor habits
Solution Approach 1:
The patent replaces manual annotation with an automated NLP system that consistently applies named entity recognition rules, eliminating variations caused by different doctors' habits and reducing omissions and mistakes while maintaining high annotation accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where annotation results are continuously evaluated and refined. The named entity recognition model learns from annotated data and improves its accuracy over time, ensuring consistent and reliable annotation quality across all reports.
3Ease of operation
If annotated report text maintains the arrangement of the original text, then the original format is preserved, but it occupies a large amount of storage space and is not conducive to the query of the report
Solution Approach 1:
The patent segments the annotated report text into structured components based on named entities and their relationships. Instead of storing the entire annotated text as a single block, the system extracts and organizes key information into discrete data elements that can be efficiently stored and queried, reducing storage requirements while improving retrieval efficiency.
Solution Approach 2:
The system extracts key named entities and their attributes from the annotated text and stores them in a structured format separate from the original text. This extraction process creates a compact representation of the report's key information that is much more space-efficient and query-friendly than storing the complete annotated text.
Data Source
AI summary
The present invention discloses a method, device and medium for structuring a capsule endoscopy report text. The method includes: annotating the report text using an annotation model; storing each named entity classification label in the report text in a hierarchical tree structure according to the annotation information to form a tree structure diagram; parsing the tree structure diagram, extracting abnormal structure data and time parameters, and storing the abnormal structure data and time parameters in a panel data structured manner to form an abnormal structure panel table and a time parameter panel table. The present invention can automatically annotate the capsule endoscopy report through the annotation model, and output parameters of different amount of information in different structures, and quantitative and accurate quality control of the capsule endoscopy process and examination results, which provide sufficient convenience for the electronic medical information of the capsule endoscopy.