Medical Record Anonymization via Deep Learning Templatization
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Solution Overview
Problem
Current medical record deidentification techniques are inefficient and ineffective in accurately identifying Protected Health Information (PHI) across various types of medical records, including structured, unstructured, and semi-structured documents, due to their reliance on traditional methods that require constant rule updates and struggle with data heterogeneity.
Innovation Solution
A method and system that combines rules, deep learning, and smart templatization to anonymize medical records by tokenizing, templatizing, and classifying PHI using a trained classifier and model, enabling efficient identification and replacement of PHI across different types of medical records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deidentification techniques are used, then the process is simpler to implement, but the accuracy of PHI identification is low and performance is poor
Solution Approach 1:
The patent combines multiple approaches (rule-based methods, machine learning models, and deep learning architectures) into a unified deidentification system. The ensemble classifier integrates predictions from multiple component models to achieve superior PHI identification accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system uses composite modeling approaches by combining different types of neural network layers (embedding layers, convolutional layers, recurrent layers) and integrating multiple classification strategies to create a robust deidentification framework that outperforms individual techniques.
2Productivity
If traditional rule-based methods are used, then the system is easier to maintain, but constant rule updates are required and performance is limited
Solution Approach 1:
The system employs self-training mechanisms where the model automatically learns from data and improves its performance over time without requiring manual rule updates. The deep learning architecture adapts to new PHI patterns and medical record formats autonomously, eliminating the need for continuous human intervention for rule maintenance.
Solution Approach 2:
The system transitions from static rule-based methods to dynamic machine learning models that can adapt and evolve. The models are designed to learn from new data continuously, allowing the system to maintain high performance as medical record structures and PHI formats change over time.
3Measurement precision
If traditional methods are used, then the computational resources required are fewer, but the recall and precision of PHI identification are insufficient
Solution Approach 1:
The deidentification process is divided into multiple stages: preprocessing, feature extraction, classification, and post-processing. This segmentation allows the system to optimize computational resources at each stage, applying more intensive processing only where necessary to achieve high precision and recall.
Solution Approach 2:
The system performs preliminary processing steps such as tokenization, lowercasing, and stopword removal before main classification. These preliminary actions prepare the data in a way that reduces the computational burden of subsequent complex modeling while improving the overall accuracy of PHI identification.
4Adaptability or versatility
If deidentification is performed on diverse medical record formats, then the system becomes more versatile, but the complexity of handling different structures increases
Solution Approach 1:
The system is designed with universal components that can process multiple types of medical record formats (structured, semi-structured, and unstructured data). The deep learning architecture handles various data representations uniformly, allowing the system to work with diverse formats without requiring format-specific processing logic.
Solution Approach 2:
The system adapts to different medical record structures by dynamically adjusting processing parameters and model configurations. The architecture can modify its behavior based on the input data characteristics, enabling versatile handling of heterogeneous medical records while maintaining consistent performance.
Data Source
AI summary
The present disclosure describes a method, apparatus, and computer readable medium for anonymizing medical records using a combination of deep learning and smart templatization. The method comprises performing tokenization on an input medical record comprising one or more sentences to generate tokenized data and generating one or more templatized sentences by performing templatization on the tokenized data, where performing the templatization comprises replacing one or more known patterns in the tokenized data with predefined patterns. The method further comprises identifying one or more PHI sentences from the templatized sentences using a trained classifier, each PHI sentence may comprise one or more PHI. The method further comprises identifying the PHI in the medical record by processing the identified PHI sentences using a trained model and generating an anonymized medical record by anonymizing the identified PHI in the input medical record.