De-identification of clinical free text using natural language
  processing: A systematic review of current approaches
Aleksandar Kovačević Bojana Bašaragin Nikola Milošević Goran Nenadić
Background: Electronic health records (EHRs) are a valuable resource for
data-driven medical research. However, the presence of protected health
information (PHI) makes EHRs unsuitable to be shared for research purposes.
De-identification, i.e. the process of removing PHI is a critical step in
making EHR data accessible. Natural language processing has repeatedly
demonstrated its feasibility in automating the de-identification process.
Objectives: Our study aims to provide systematic evidence on how the
de-identification of clinical free text has evolved in the last thirteen years,
and to report on the performances and limitations of the current
state-of-the-art systems. In addition, we aim to identify challenges and
potential research opportunities in this field. Methods: A systematic search in
PubMed, Web of Science and the DBLP was conducted for studies published between
January 2010 and February 2023. Titles and abstracts were examined to identify
the relevant studies. Selected studies were then analysed in-depth, and
information was collected on de-identification methodologies, data sources, and
measured performance. Results: A total of 2125 publications were identified for
the title and abstract screening. 69 studies were found to be relevant. Machine
learning (37 studies) and hybrid (26 studies) approaches are predominant, while
six studies relied only on rules. Majority of the approaches were trained and
evaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most
frequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016
CEGS N-GRID (10 studies) corpora.