Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results

There is an increasing interest in exploiting the content of electronic health records by means of natural language processing and text-mining technologies, as they can result in resources for improving patient health/safety, aid in clinical decision making, facilitate drug repurposing or precision medicine. To share, re-distribute and make clinical narratives accessible for text mining research purposes, it is key to fulfill legal conditions and address restrictions related data protection and patient privacy. Thus, clinical records cannot be shared directly ”as is”. A necessary precondition for accessing clinical records outside of hospitals is their de-identification or exhaustive removal/replacement of all mentioned privacy related protected health information phrases. Providing a proper evaluation scenario for automatic anonymization tools is key for approval of data redistribution. The construction of manually de-identified medical records is currently the main rate and cost-limiting step for secondary use applications of clinical data. This paper summarizes the settings, data and results of the first shared track on anonymization of medical documents in Spanish, the MEDDOCAN (Medical Document Anonymization) track. This track relied on a carefully constructed synthetic corpus of clinical case documents, the MEDDOCAN corpus, following annotation guidelines for sensitive data based on the analysis of the EU General Data Protection Regulation. A total of 18 teams (from the 51 registrations) submitted 63 runs for first sub-track 1 and 61 systems for the second sub-track. The top scoring systems were based on sophisticated deep learning approaches, representing strategies that can significantly reduce time and costs associated to accessing textual data containing privacy-related sensitive information. The results of this track might help in lowering the clinical data access hurdle for Spanish language technology developers, showing also potentials for similar settings using data in other languages or from different domains.