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  • Vakili, T., Hullmann T., Henriksson A. and H. Dalianis. 2024. When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification. To be presented at the CALD-pseudo Workshop at the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, Malta.
  • Ngo, P., Tejedor M., Olsen Svenning T., Chomutare T., Budrionis A. and H. Dalianis. 2024. Deidentifying a Norwegian clinical corpus – An effort to create a privacy-preserving Norwegian large clinical language model. To be presented at the CALD-pseudo Workshop at the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, Malta.
  • Lamproudis, A., Mora, S., Olsen Svenning T., Torsvik T., Chomutare T., Dinh Ngo P. and H. Dalianis. 2023. De-identifying Norwegian Clinical Text using Resources from Swedish and Danish. Proceedings of AMIA 2023, Annual Symposium, November 11-15. New Orleans, LA, USA, link.
  • Vakili, T. and H. Dalianis. 2023. Using Membership Inference Attacks to Evaluate Privacy-Preserving Language Modeling Fails for Pseudonymizing Data. Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa 2023). Faroe Islands, May 22-24, 2023, link.
  • Vakili, T., Lamproudis, A., Henriksson, A. and H. Dalianis. 2022. Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data. In the Proceedings of the 13th International Conference on Language Resources and Evaluation, LREC 2022, Marseille, France, pp. 4245–4252, link.
  • Vakili, T. and H. Dalianis 2022, Utility Preservation of Clinical Text After De-Identification. In the Proceedings of the 21st Workshop on Biomedical Language Processing (pp. 383-388) in conjunction with ACL 2022, Dublin, Ireland, link.
  • Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund, Quantifying Membership Privacy via Information Leakage, IEEE Transactions Information Forensics and Security. Vol.16, pp. 3096-3108, 2021, link.
  • Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund, Optimal Maximal Leakage-Distortion Tradeoff. Information Theory Workshop (ITW) 2021 IEEE, pp. 1-6, 2021, link.
  • Vakili, T. and H. Dalianis. 2021. Are Clinical BERT Models Privacy-Preserving? The Difficulty of Extracting Patient-Condition Associations. In the Proceedings of the Association for the Advancement of Artificial Intelligence AAAI Fall 2021 Symposium in HUman partnership with Medical Artificial iNtelligence (HUMAN.AI), November 4-6, 2021, pdf.
  • Lamproudis, A., Henriksson, A. and H. Dalianis. 2021. Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data. In the Proceeding of RANLP 21: Recent Advances in Natural Language Processing, 1-3 Sept 2021, Varna, Bulgaria, pdf.
  • Grancharova, M. and H. Dalianis. 2021. Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records. In the Proceedings of the 23rd Nordic Conference on Computational Linguistics, NoDaLiDa 2021, Iceland, May 31 – June 2, 2021, pdf.
  • Dalianis, H. and H. Berg. 2021. HB Deid – HB De-identification tool demonstrator. In the Proceedings of the 23rd Nordic Conference on Computational Linguistics, NoDaLiDa 2021, Iceland, May 31 – June 2, 2021, pdf.
  • Berg, H., Henriksson, A., Fors, U. and H. Dalianis. 2021. De-identification of Clinical Text for Secondary Use: Research Issues. In the proceedings of HEALTHINF 2021, 14th International Conference on Health Informatics Feb 11-13, 2021, pdf.
  • Grancharova, M., Berg, H. and H. Dalianis. 2020. Improving Named Entity Recognition and Classification in Class Imbalanced Swedish Electronic Patient Records through Resampling. Compilation of abstracts in The Eight Swedish Language Technology Conference (SLTC-2020), Göteborg, pdf.
  • Berg, H., A.Henriksson and H. Dalianis. 2020. The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, Louhi 2020, in conjunction with EMNLP 2020, (pp. 1-11), pdf.
  • Berg, H., Henriksson, A., Fors, U. and H. Dalianis. De-identification of Clinical Text for Secondary Use: Research Issues. Presented at the Healthcare Text Analytics Conference HealTAC 2020, April 23, London.
  • Berg, H. and H. Dalianis. 2020. A Semi-supervised Approach for De-identification of Swedish Clinical Text. Proceedings of 12th Conference on Language Resources and Evaluation, LREC 2020, May 13-15, Marseille, pp. 4444‑4450, pdf.
  • Berg, H., T. Chomutare and H. Dalianis. 2019. Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text. In the Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis, Louhi 2019, in conjunction with Conference on Empirical Methods in Natural Language Processing, (EMNLP) November 2019, Hongkong, ACL, pp 118-125, pdf.
  • Berg, H. and H. Dalianis. 2019. Augmenting a De-identification System for Swedish Clinical Text Using Open Resources (and Deep learning). In the Proceedings of the Workshop on NLP and Pseudonymisation, in conjunction with the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), Turku, Finland, September 30, 2019, pdf.
  • Dalianis, H. 2019. Pseudonymisation of Swedish Electronic Patient Records Using a Rule-based Approach. In the Proceedings of the Workshop on NLP and Pseudonymisation, in conjunction with the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), Turku, Finland, September 30, 2019, pdf.