Payer > Data Privacy

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  Detect PHI for Deidentification

  • Published on: March, 2021
  • John Snow Labs
  • Code Available

  Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word em ...

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  Differentially Private Synthetic Data-Applied Evaluations and Enhancements

  • Published on: March, 2020
  • Samira Pouyanfar, Anuj Desai, Eduardo de Leon, Joshua Allen, Lucas Rosenblatt, Xiaoyan Liu
  • Code Available

  When collecting sensitive information from groups or organization, it is highly important to maintain an individual's privacy. A formalization of priv ...

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  Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

  • Published on: January, 2018
  • Siddharth Biswal, Edward Choi, Walter F. Stewart, Bradley Malin, Jimeng Sun, Jon Duke
  • Code Available

  Researchers have a controlled access to the Electronic Health Records (EHR) data as it is composed of personal identifiers and sensitive medical info ...

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  Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records

  • Published on: May, 2021
  • Christin Seifert, Jan Trienes, Claudia Alessandra Libbi, Dolf Trieschnigg
  • Code Available

  Synthetic data presents a promising solution to the privacy concern, if synthetic data has comparable utility to real data and if it preserves the pri ...

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  CorGAN-Correlation Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records

  • Published on: February, 2020
  • Edward A. Fox, Amirsina Torfi
  • Code Available

  In healthcare domain, researchers face a lot of privacy challenges when it comes to create a deep learning model using electronic health record (EHR) ...

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  GAIN-Missing Data Imputation using Generative Adversarial Nets

  • Published on: January, 2018
  • Mihaela van der Schaar, James Jordon, Jinsung Yoon
  • Code Available

  GAIN comprises of a method for imputing missing data by adapting the Generative Adversarial Nets (GAN) framework. GAN models consists of generator and ...

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  Differentially Private Synthetic Medical Data Generation using Convolutional GANs

  • Published on: December, 2020
  • Chandan K. Reddy, Edward A. Fox, Amirsina Torfi
  • Code Available

  Torfi et. al. have built a privacy-preserving GAN model using Renyi differential privacy for generating synthetic medical data from real health record ...

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  DP-CGAN-Differentially Private Synthetic Data and Label Generation

  • Published on: February, 2018
  • Kaixiang Lin, Liyang Xie, Fei Wang, Jiayu Zhou, Shu Wang
  • Code Available

  Generative Adversarial Network (GAN) provide a promising direction in the studies where data availability is limited. However, GANs can easily remeber ...