CRFs based de-identification of medical records.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 26315662.
- Also identified by DOI 10.1016/j.jbi.2015.08.012 and PMC identifier 4988860.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level.
Medical subject headings
- Computer Security
- Confidentiality
- Data Mining
- Electronic Health Records
- Natural Language Processing
- Pattern Recognition, Automated