Anonymizing Heterogeneous Clinical Reports via CRF Sequence Classification
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Solution Overview
Problem
Current solutions for anonymizing medical reports, such as Deid PhysioNet and MITRE Identification Scrubber Toolkit, are complex, inefficient, and not easily adaptable for use in hospitals, as they require manual effort and are limited to specific formats, making it difficult to effectively remove Protected Health Information (PHI) across various document formats and structures.
Innovation Solution
A machine-learned system using a conditional random field sequence classifier for natural language processing (NLP) that integrates report format conversion, annotation, training, and application, allowing for the identification and removal of PHI in medical reports across heterogeneous formats, with a modular design for collaborative and scalable anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual anonymization methods are used, then PHI can be removed from medical reports, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual mechanical anonymization processes with an automated machine-learning system. The system uses trained models to automatically identify and redact PHI in medical reports, eliminating the need for manual review and reducing complexity while maintaining reliability.
Solution Approach 2:
The anonymization system is designed to operate autonomously without requiring manual intervention. The machine-learning models self-adjust and improve through continuous training on annotated data, and the system automatically handles the entire anonymization workflow from input to output.
2Extent of automation
If existing NLP tools like Deid are used, then PHI identification can be automated, but the tools are limited to specific formats and require extensive customization
Solution Approach 1:
The patent creates a universal anonymization system that can process multiple document formats including PDF, DOCX, TXT, and DICOM reports. The system uses format-detection logic to automatically adapt to different input types, eliminating the need for separate tools for each format and enabling broad versatility.
3Measurement precision
If comprehensive PHI lists are created for NER models, then identification accuracy improves, but the process becomes tedious and incomplete
Solution Approach 1:
The patent implements a two-stage approach where common PHI patterns are pre-configured in the system before use. This preliminary setup provides immediate functionality for standard PHI types, while allowing users to add custom patterns as needed. The system is ready to operate with basic functionality before any customization, reducing initial setup time.
4Reliability
If manual review of each report is performed to guarantee PHI removal, then completeness is ensured, but effort and complexity remain high
Solution Approach 1:
The patent incorporates feedback mechanisms where the system logs anonymization results and allows for error reporting. When users identify missed PHI or false positives, the system uses this feedback to retrain and improve its models, continuously enhancing reliability without requiring manual review of every report.
Data Source
AI summary
For anonym izing or other keyword identification medical patient data, a conditional random field sequence classifier is used for the NER model for NLP, providing a technical solution to help the computer perform better at identifying PHI from context and reduce manual anonym ization efforts of medical reports. One tool or executable integrates report format conversion, annotation, training, and application. These operations may be selected, or the tool configured for anonymization or keyword identification. Different files from each stage may be exported or used by others operating on other computers, allowing collaboration or sequential burden sharing for anonym ization.