Invited speaker

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Martin Krallinger

Head of the Text Mining unit, Barcelona Supercomputing Center (BSC)


Short Bio

Martin Krallinger is currently the head of the Text Mining unit at the Barcelona Supercomputing Center (BSC), and former head of the Biological Text Mining unit of the Spanish National Cancer research Centre (CNIO). He is an expert in the field of biomedical and clinical text mining and language technologies and has been working in this and related research topics since more than ten years, which resulted in over 70 publications and several domain specific text mining applications for drug-safety, molecular systems biology and oncology, etc. He was involved in the implementation and evaluation of biomedical named entity recognition components, information extraction systems and semantic indexing of large datasets of heterogeneous document types (research literature, patents, legacy reports, European public assessment reports). His research interest, besides clinical NLP include text-mining assisted biocuration, interoperability standards and formats for biomedical text annotations (BioC) as well as development of efficient text annotation infrastructures. He also promoted the development of the first biomedical text annotation meta-server (Biocreative metaserver - BCMS) and the follow up BeCalm/TIPS metaserver.  He is one of the main organizers of BioCreative community assessment challenges for the evaluation of biomedical NLP systems and has been involved in the organization of text mining shared tasks in various international community challenge efforts including IberEval, IberLEF, and CLEF.



There is an increasing interest in exploiting the content of unstructured clinical narratives by means of language technologies and text mining. To be able to share, re-distribute and make clinical narratives accessible for text mining and NLP research purposes it is key to fulfill legal conditions and address restrictions related data protection and patient privacy legislations. Thus clinical records with protected health information (PHI) cannot be directly shared “as is”, due to privacy constraints, making it particularly cumbersome to carry out NLP research in the medical domain. A necessary precondition for accessing clinical records outside of hospitals is their de-identification, i.e., the exhaustive removal  (or replacement) of all mentioned PHI phrases.

Providing a proper evaluation scenario of automatic anonymization tools, with well-defined sensitive data types is crucial for approval of data redistribution consents signed by ethical committees of healthcare institutions. Moreover, it is important to highlight that the construction of manually de-identified medical records is currently the main rate and cost-limiting step for secondary use applications.

This talk will summarise the settings, data and results of the first community challenge task specifically devoted to the anonymization of medical documents in Spanish, called the MEDDOCAN (Medical Document Anonymization) task, as part of the upcoming IberLEF evaluation initiative. This track relied on a synthetic corpus of clinical case documents called the MEDDOCAN corpus. In order to carry out the manual annotation of this corpus we have constructed the first public annotation guidelines for PHI in Spanish carefully examining the specifications derived from the EU General Data Protection Regulation (GDPR). From the 51 registered teams, covering participants both from academia and companies, a total of 18 teams have submitted runs for this track. The top scoring runs represent very competitive approaches than can significantly reduce time and costs associated to the access of textual data containing privacy-related sensitive information. This talk will conclude with a summary of the methodologies used by participating teams to automatically identify sensitive information, together with lessons learned and future steps.