1. De-identification of Medical Narrative Data
Introduction
De-identification
In Natural Language Processing (NLP), de-identification is the process of masking or removing a certain number of identifiers (i.e. pieces of information which could directly or in combination reveal the identity of the subject). Commonly, the choice of identifiers to be removed is based on the list of 18 identifiers provided by the Health Insurance Portability and Accountability Act (HIPPA) of the American legislation.
Why ?
Privacy Protection
Decreases the risk of re-identification
Promote Research
No need for consent or waiver of consent
Previous work
De-identification has been performed on medical free text for over 20 years with a growing capacity to de-identify various types of medical documents and an ability to process larger amounts of information. Initially performed by hand, de-identification has been automated using rule based systems and more recently machine-learning (or mixed) methods.
De-identified information ≠ Anonymous information
Methods
Rule-based method
Named Entity Recognition (NER) task
De-identification of PHI via finite-state automata
Replacement of de-identified PHI by surrogate information
Tags on de-identified PHI: <patient>, <doctor>, <location>, <date>
Examples of de-identified text
Original text
De-identified text
Madame Foufi sera suivie à la consultation de diabétologie aux HUG du 08.2 au 09/02/2019.
Madame Cartier <patient> sera suivie à la consultation de diabétologie aux Hôpitaux <location> du au 30/02/2019 <date>.
Mme Foufi a été transférée à la clinique de Joli-Mont le 06 janvier 2014.
Mme Cartier <patient> a été transférée à la Clinique <location> le 30 février 2014 <date>.
Results
De-identified PHIs in 11’000 discharge summaries Results evaluation on 1’000 discharge summaries
Dates
Patients’ names
Doctors’ names
Locations
Nurses’ names & nursing care services
Telephone & fax numbers
Addresses
Health Insurance companies
29,221
27,357
4,685
7,907
483 83 5 3
Dates
Patients’ names
Doctors’ names
Locations
Overall performance
Precision
0.9889
0.9970 1
0.9628
0.9907
Recall
0.9228
0.9916
0.9876
0.7872
0.9342
Conclusion
Next steps
Processing of time intervals
De-identification, manual validation and evaluation on larger corpora
Adapt de-identification rules to other languages (IT, GER)
Remarks
De-identification is a challenging task
Our de-identification tool achieved a good performance
Errors in discharge summaries affect the de-identification process
Vasiliki FOUFI, PhD
Christophe GAUDET-BLAVIGNAC, Bmed, Mmed, BSc CS
Raphaël CHEVRIER, Bmed, Mmed
Christian LOVIS, MD, MPH, FACMI