title: De-identification of patient notes with recurrent neural networks.
abstract: 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 that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of electronic health record databases, the limited number of researchers with access to non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value.
  
  
  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 of 97.38 and a precision of 98.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall of 99.25 and a precision of 99.21.
  
  
  CONCLUSIONS
  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 manual feature engineering.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Franck
authors.initials: F
authors.lastname: Dernoncourt
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Ji Young
authors.initials: JY
authors.lastname: Lee
authors.affiliation: Computer Science Department, University at Albany, SUNY, Albany, NY, USA.
authors.forename: Ozlem
authors.initials: O
authors.lastname: Uzuner
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
mesh_terms: D000068240:Data Anonymization; D066264:Datasets as Topic; D057286:Electronic Health Records; D006801:Humans; D016571:Neural Networks, Computer
keywords: de-identification; medical language processing; neural networks