State-of-the-art anonymization of medical records using an iterative machine learning framework.

@article{Szarvas2007StateoftheartAO,
  title={State-of-the-art anonymization of medical records using an iterative machine learning framework.},
  author={Gy{\"o}rgy Szarvas and Rich{\'a}rd Farkas and R{\'o}bert Busa-Fekete},
  journal={Journal of the American Medical Informatics Association},
  year={2007},
  volume={14},
  pages={574-580}
}
Objective: The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act. Design: We… 

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