Annotated training data

• Annotated clinical text in Swedish for training and evaluation of machine learning tools:
  Annoteringar gjorda 2008–2023, ver30.pdf (273 Kb)
  The annotated data is clinical text written in Swedish. It has been annotated the years 2008-2023. The descriptions are in Swedish, but can be understood since the annotation classes are in English and there are numerical descriptions. For more information contact author Hercules Dalianis.

Tools

• Health Bank Deid - HB Deid - de-identification and pseudonymisation tool for Swedish text
• ICD-10 coder
  ICD-10 diagnosis code assignment tool
• Medical Decompounder for Swedish (164 Kb)
  Petter Andersson and Amanda Sjöberg. 2016. Generating and evaluating an automatic mapping between SNOMED-CT and the Swedish extension codes of ICD-10 based on lexical similarities, Master thesis, DSV, Stockholm University.
• DrugView Tools 
  DrugView Demo is a tool that presents and explores drug-pairs from Swedish Electronic Records from the years 2009-2010.

Other resources

• A de-identified clinical Swedish Language Model SweDeClin-BERT. Please contact Hercules Dalianis for access.
  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 Proceedings of the 13th International Conference on Language Resources and Evaluation, LREC 2022, Marseille, France, pp. 4245–4252.
• Swedish ICD-10 diagnosis codes in text format
• Swedish negation triggers for NegEx
• Swedish ADE word lists (10 Kb)
  Freidrich, S. and H. Dalianis. 2015. Adverse Drug Event classification of health records using dictionary based pre-processing and machine learning In the proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis, Louhi, held in conjunction with EMNLP 2015, Lisbon, Portugal, pp 121-130. 
• Swedish Medical Abbreviations (15 Kb)
  Kvist, M., and S. Velupillai. 2014. SCAN: A Swedish Clinical Abbreviation Normalizer  In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 62-73). Springer International Publishing.

Master theses and reports

• Sonja Remmer. 2021. Automatic Diagnosis Code Assignment with KB-BERT — ICD Classification Using Swedish Discharge Summaries, Master Thesis, Stockholm University.
• Synnøve Bråten. 2020. Extending a Synthetic Norwegian Clinical Corpus for De-Identification, Master Thesis, Stockholm University/Karolinska Institutet. Norwegian clinical synthetical corpus, NorSynthClinical-PHI, GitHub