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Media preprint DOI: https://doi.org/10.1101/2020.08.09.20171231; this version posted August 11, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder,
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How to fill out customize deep learning-based de-identification

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Collect a dataset of annotated medical records for de-identification
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Pre-process the data by removing any identifiable information
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Build and train a deep learning model on the pre-processed data
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Fine-tune the model to improve performance and accuracy
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Test the model on a separate dataset to evaluate its de-identification capabilities
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Deploy the customized deep learning model for de-identification purposes

Who needs customize deep learning-based de-identification?

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Healthcare organizations dealing with sensitive patient data
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Research institutions working with medical records
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Companies providing services for de-identifying personal information
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Customize deep learning-based de-identification is a process of using deep learning techniques to remove or anonymize sensitive information from data.
Entities or organizations handling sensitive data are required to employ customize deep learning-based de-identification to protect privacy.
Customize deep learning-based de-identification can be filled out by using pre-trained models or by training new models on the specific data sets.
The purpose of customize deep learning-based de-identification is to protect the privacy of individuals by removing or obfuscating sensitive information from datasets.
The information that must be reported on customize deep learning-based de-identification includes the techniques used, the data sets processed, and the results achieved.
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