NLP Models for CAD Information Extraction
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Solution Overview
Problem
Current methods struggle to automatically extract relevant coronary artery disease (CAD) information from unstructured medical data, which hinders the identification of patients at high risk for complications during percutaneous coronary interventions (PCIs).
Innovation Solution
The use of trained natural language processing (NLP) models, including named entity recognition (NER) and relation extraction (REL) models, to process unstructured medical text and extract CAD information, such as predicted coronary lesion information, from echocardiographs and angiography reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction methods are used to obtain CAD information from medical data, then extraction accuracy may be maintained through expert review, but the process is time-consuming and cannot efficiently identify high-risk patients
Solution Approach 1:
The patent replaces manual mechanical review processes with automated NLP models (machine learning and deep learning systems) that can process medical texts, imaging reports, and electronic health records to extract CAD information automatically, thereby increasing productivity without proportional time investment
Solution Approach 2:
The system creates digital copies and representations of CAD information from various medical data sources (texts, images, reports) and processes these copies through NLP models to identify high-risk patients, enabling parallel processing of multiple patient records simultaneously
2Productivity
If automated extraction methods are implemented, then processing speed increases, but the accuracy of extracting relevant CAD information from unstructured data deteriorates
Solution Approach 1:
The patent segments the automated extraction process into multiple specialized NLP models that handle different aspects of CAD information extraction (entity recognition, relation extraction, attribute identification), allowing each model to specialize in specific tasks and maintain high accuracy while processing data in parallel
Solution Approach 2:
The system incorporates feedback mechanisms where NLP model outputs are validated and refined through iterative processing, with results from one model serving as input for subsequent models, and with capabilities for manual review and correction that feed back into model training to improve accuracy over time
3Reliability
If comprehensive CAD information is extracted from all available unstructured medical data, then patient risk assessment completeness improves, but system complexity increases
Solution Approach 1:
The patent develops universal NLP models that can process multiple types of unstructured medical data (texts, imaging reports, EHR entries) through a common framework, allowing the system to comprehensively extract CAD information from diverse sources without proportionally increasing complexity
Solution Approach 2:
The system implements a nested architecture where specialized NLP models (for specific CAD attributes or data types) are integrated within a comprehensive multi-model system, with output from inner models serving as input to outer models, creating a hierarchical structure that manages complexity while maintaining completeness
Data Source
AI summary
extracting coronary artery disease (CAD) information from unstructured medical text are provided. The method includes receiving unstructured medical text, processing the unstructured medical text using one or more trained natural language processing (NLP) models, wherein the one or more trained NLP models are trained to output CAD information, wherein the CAD information includes predicted coronary lesion information, and displaying an indication of the predicted coronary lesion information on a user interface associated with a computing device.