Medical Data Anonymizer Module

Description

The Medical Data Anonymizer Module is a 3D Slicer extension designed to anonymize medical data in text files. It focuses on removing personal identifiers, such as patient names and dates of birth, while retaining other critical clinical information. This ensures that the data remains useful for research and analysis while protecting patient privacy.

This module uses the spaCy library for natural language processing (NLP) to identify and anonymize personal information. Specifically, it utilizes the en_core_web_sm pre-trained model for named entity recognition (NER) to detect names, dates, and other entities within the text.

Installation

To install the Medical Data Anonymizer Module, follow these steps:

1. Using 3D Slicer Extension Manager:

   • Open 3D Slicer.
   • Go to Edit > Application Settings > Modules.
   • Click Add Module Path and select the folder containing the Medical Data Anonymizer Module.
   • Restart 3D Slicer to apply the changes.

2. Manual Installation:

   • Clone this repository to your local machine:

     git clone https://github.com/YourGitHubUsername/MedicalDataAnonymizer.git

   • In 3D Slicer, go to Edit > Application Settings > Modules.
   • Click Add Module Path and select the folder where you cloned the repository.
   • Restart 3D Slicer to apply the changes.

Usage

Once the module is installed, follow these steps to anonymize your medical data:

1. Open 3D Slicer.
2. Navigate to the Medical Data Anonymizer module from the module dropdown.
3. In the Files to be Anonymized section, select the directory containing the text files you want to anonymize. TYPE OF FILE: .DOCX
4. In the Output Anonymized Files section, choose the directory where you want to save the anonymized files.
5. Click the Install Dependencies button to ensure all necessary packages are installed.
6. After dependencies are installed, click the Anonymize button to start the anonymization process.
7. The anonymized files and a CSV file mapping the original filenames to the anonymized filenames will be saved in the output directory.

Contributors

• Jonas Bianchi - Developer and Maintainer

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

Acknowledgments

This module leverages the spaCy library and its en_core_web_sm pre-trained model for natural language processing. We acknowledge the spaCy team for their powerful and user-friendly NLP tools.