Cross-Clinic De-Identification of Swedish Electronic Health Records : Nuances and Caveats

• Privacy preservation of sensitive information is one of the main concerns in clinical text mining. Due to the inherent privacy-keeping problems that arise when handling clinical data, the clinical corpora used to create the clinical Named Entity Recognition (NER) models underlying clinical de-identification systems cannot be shared. This implies that clinical NER models are trained and tested on data coming from the same institution because it is rarely possible to evaluate them on data belonging to a different institution. Given this sharing restrictions, it is very to assess whether a clinical NER model has overfitted the data or if it is driven by undetected biases. In this paper we present the results of the first-ever cross-institution evaluation of a Swedish de-identification system on Swedish clinical data. Alongside the encouraging results, we present a discussion about differences and similarities across EHR naming conventions and NER tagsets.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

Nyckelord

de-identification

clinical NLP

NER

electronic health records

cross-clinic evaluation

data- och systemvetenskap

Computer and Systems Sciences

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)