  Objective: Patient notes in electronic health records (EHRs) may contain
critical information for medical investigations. However, the vast majority of
medical investigators can only access de-identified notes, in order to protect
the confidentiality of patients. In the United States, the Health Insurance
Portability and Accountability Act (HIPAA) defines 18 types of protected health
information (PHI) that needs to be removed to de-identify patient notes. Manual
de-identification is impractical given the size of EHR databases, the limited
number of researchers with access to the non-de-identified notes, and the
frequent mistakes of human annotators. A reliable automated de-identification
system would consequently be of high value.
  Materials and Methods: We introduce the first de-identification system based
on artificial neural networks (ANNs), which requires no handcrafted features or
rules, unlike existing systems. We compare the performance of the system with
state-of-the-art systems on two datasets: the i2b2 2014 de-identification
challenge dataset, which is the largest publicly available de-identification
dataset, and the MIMIC de-identification dataset, which we assembled and is
twice as large as the i2b2 2014 dataset.
  Results: Our ANN model outperforms the state-of-the-art systems. It yields an
F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision
of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with
a recall 99.25 and a precision of 99.06.
  Conclusion: Our findings support the use of ANNs for de-identification of
patient notes, as they show better performance than previously published
systems while requiring no feature engineering.
