HB D eid - HB De-identification tool demonstrator (2021)

Abstract: This paper describes a freely available web-based demonstrator called HB Deid. HB Deid identifies so-called protected health information, PHI, in a text written in Swedish and removes, masks, or replaces them with surrogates or pseudonyms. PHIs are named entities such as personal names, locations, ages, phone numbers, dates. HB Deid uses a CRF model trained on non-sensitive annotated text in Swedish, as well as a rule-based post-processing step for finding PHI. The final step in obscuring the PHI is then to either mask it, show only the class name or use a rule-based pseudonymisation system to replace it.

## introduction electronic patient records and other health data contain information that can identify a patient. these data need to be washed, for both legal and ethical reasons before they can be re-used for various purposes as research or machine learning. this paper presents a machine learning-based demonstrator called hb deid, health bank de-identification tool. it is a tool for automatic deidentification and pseudonymisation of protected health information, phi. phis are information that can identify a patient. phis are similar to named entities and encompass for example personal names, locations, phone numbers, dates, ages, etc. phis can be present both in structured and unstructured clinical data such as free text. in structured data phis are easily identifiable, the table information informs of the category of the data the whole table can be removed or obscured. in the free clinical text there is a greater effort to identify hidetext, http://www.hidetext.se is the platform where hb deid is commercialised. a phi since, for example, the phi entity parkinson is difficult to classify whether it is a disease name or a personal name. similarly, sjögren's in sjögren's syndrome may be mistaken for a patient's name. in this paper, we focus on phis foot_0 in free text in electronic patient records. a demonstrator is a pedagogical instrument to show a system or an idea and let a broader audience explore -in our case -a research system or pilot system. the process of de-identifying text typically uses two steps. firstly, a phi is identified through named entity recognition. secondly, the phi is obscured. strategies for hiding information may be masking the information or replacing it with a surrogate through a process called pseudonymisation. pseudonymisation makes the text more fluent to read and when also removing the annotation tags it inconceives the identification of potentially remaining sensitive data in plain sight and protects the identification of phis. this method is called hiding in plain sight (hips), @xcite . a substantial amount of studies have been published on the de-identification of text @xcite . while most of these studies have focused on english, research have been carried out for french @xcite , spanish @xcite , danish @xcite and swedish @xcite as well as japanese @xcite . generally, high recall is preferred over high precision in de-identification research as the privacy of the individuals describe is of paramount importance. it is therefore important to not miss any sensitive information. with regards to pseudonymisation, there are fewer studies. one of the first was a study by @xcite , which described a system for identifying phi and then replacing them with surrogates, but not how this process was carried out. in another study by @xcite this pseudonymisation process is described elaborated such as that dates were shifted, personal names were shifted to other personal names in the boston area. locations were shifted randomly, and hospitals were given fictitious names. for de-identification and pseudonymisation for english there is yet another study @xcite . for pseudonymisation for swedish, there is a system described in @xcite . while there are plenty of demonstrators of named entity recognition systems the only available one for de-identification, to our knowledge, is the hitzalmed demonstrator @xcite . the hitzalmed demonstrator is constructed for de-identification and pseudonymisation of spanish electronic patient records. to try it out a registration must be carried out here foot_1 . the system identifies and categorizes entities into different categories, as: first name, last name, location, phone number, age, date and health care unit. while the system is intended for spanish, this part works relatively well for english and swedish. the system then either masks or replaces the sensitive information with surrogates -which are in spanish. ## the hb deid demonstrator the hb deid demonstrator foot_2 , see figure 1 , is an attempt to show the possibilities of de-identification and pseudonymisation techniques for electronic patient records written in swedish. the system is based on the work carried out by @xcite for de-identification and by @xcite for the pseudonymisation. the data used to train hb deid is not the original set of annotated sensitive electronic patient records in swedish -the stockholm epr phi corpus, but the corpus is indirectly used since its trained model is used to improve the public available swedish news corpora called webbnyheter 2012 that are semi-manually annotated for the ner classes per and loc and org and misc foot_3 . webbnyheter 2012 was machine annotated using the original sensitive trained model from stockholm epr phi corpus and then it was corrected manually, using both the manual annotations and the machine annotations to decide on the correct annotation. this effort was carried out to avoid using the sensitive stockholm epr phi corpus directly. the bootstrapped model in the hb deid demonstrator has not yet been evaluated, but the original sensitive model has been evaluated and the results reported in @xcite . ## conclusion we have shown how to train and construct an automatic de-identification and pseudonymisation tool for clinical text in swedish. we have also used a bootstrapped language model that is privacy protected. finally, we have made a user friendly and freely available web interface to the demonstrator called hb deid. the demonstrator has been presented in the swedish trade magazine computer sweden, @xcite . we have also been contacted by many users that had asked us about hb deid and given us feedback on how to improve the system. they have also asked us if they can use hb deid for other purposes as de-identification of transcribed interviews. one more proposal was to have the possibility to correct wrong predictions by re-annotate them directly in the hb deid interface to re-learn hb deid. we plan to let stockholm regional council and karolinska university hospital test hb deid on their electronic patient record texts with the future aim to de-identify them before they are handed out to researchers or for machine learning purposes.