Lab Report Data Extraction Using Neural Network Segmentation
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Solution Overview
Problem
The increasing complexity and volume of medical records have rendered manual and traditional computerized data extraction methods inefficient for real-time retrieval of unstructured data from lab reports, necessitating improved computerized extraction techniques to enhance data processing efficiency and reduce computational resource demands.
Innovation Solution
A computer-implemented method utilizing a neural network, specifically a restricted Boltzmann machine, to identify and categorize lab reports, extracting relevant patient information and lab results by parsing only the necessary portions of the documents, thereby eliminating unnecessary processing steps and improving data extraction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional computerized data extraction methods are used on complex medical records, then data extraction can be automated, but computing resources and processing time increase significantly
Solution Approach 1:
The patent segments the lab report into distinct regions (patient information region, lab results region, etc.) using template matching. By dividing the document processing into regional segments with predefined templates, the system avoids analyzing the entire document uniformly, thereby reducing computational resource consumption while maintaining automation.
Solution Approach 2:
The patent performs preliminary categorization of lab reports by identifying recognizable characteristics and matching them against stored templates before detailed data extraction. This preliminary action allows the system to pre-determine which templates apply, reducing the computational burden during the actual extraction phase by avoiding unnecessary processing of irrelevant document sections.
2Extent of automation
If traditional computerized data extraction methods are used on large volumes of medical records, then automation is achieved, but processing speed decreases
Solution Approach 1:
By segmenting the lab report into predefined regions (patient information, lab results, etc.) and applying specific templates to each segment, the system processes only relevant portions of each document. This segmentation enables parallel processing of multiple regions and reduces the overall processing time, thereby increasing the data extraction rate while maintaining automation.
Solution Approach 2:
The system performs preliminary template matching and categorization before detailed extraction, which allows for optimized processing paths to be selected in advance. This preliminary action reduces the computational overhead during extraction and enables faster processing of large volumes of records by avoiding unnecessary analysis of irrelevant document sections.
3Measurement precision
If manual document processing is used, then data extraction accuracy can be maintained, but processing time becomes unacceptable for real-time retrieval
Solution Approach 1:
The patent stores templates that define the expected structure and characteristics of different lab report types in advance. By performing preliminary matching of incoming reports against these pre-stored templates, the system can rapidly categorize and extract data with high accuracy without manual intervention, achieving real-time processing speeds while maintaining extraction precision.
Data Source
AI summary
Computer program products, methods, systems, apparatus, and computing entities for extracting lab result data from lab reports are provided. In one example embodiment, an example computing device receives a lab report. The computing device identifies one or more relevant portions of the lab report. The computing device then generates parsed lab report data from only the identified one or more relevant portions of the lab report. Subsequently, the computing device extracts patient information and lab results from the parsed lab report data. Using various embodiments of the present invention, patient information and lab results can be efficiently extracted for incorporation into structured data sets maintained, for example, by a healthcare company.