Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers

Stud Health Technol Inform. 2019 Aug 21;264:283-287. doi: 10.3233/SHTI190228.

Abstract

Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learning tasks. In the context of a deep learning experiment to detect altered mental status in emergency department provider notes, we tested several classifiers on clinical notes in their original form and on their automatically de-identified counterpart. We tested both traditional bag-of-words based machine learning models as well as word-embedding based deep learning models. We evaluated the models on 1,113 history of present illness notes. A total of 1,795 protected health information tokens were replaced in the de-identification process across all notes. The deep learning models had the best performance with accuracies of 95% on both original and de-identified notes. However, there was no significant difference in the performance of any of the models on the original vs. the de-identified notes.

Keywords: Data Anonymization; Machine Learning; Natural Language Processing.

MeSH terms

  • Confidentiality
  • Data Anonymization*
  • Deep Learning*
  • Electronic Health Records
  • Humans
  • Machine Learning