1. Andersson, P., & Sjöberg, A. (2016). Generating and Evaluating an Automatic Mapping Between SNOMED-CT and the Swedish Extension Codes of ICD-10 Based on Lexical Similarities. Master’s thesis, Department of Computer and Systems Sciences, Stockholm University.
2. Aramaki, E., Miura, Y., Tonoike, M., Ohkuma, T., Mashuichi, H., & Ohe, K. (2009). Text2table: Medical text summarization system based on named entity recognition and modality identification. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (pp. 185–192). Association for Computational Linguistics.
3. Aramaki, E., Miura, Y., Tonoike, M., Ohkuma, T., Masuichi, H., Waki, K., et al. (2010). Extraction of adverse drug effects from clinical records. Studies in Health Technology and Informatics, 160(Pt 1), 739–743.
4. Aramaki, E., Morita, M., Kano, Y., & Ohkuma, T. (2014). Overview of the NTCIR-11 MedNLP-2 Task. In NTCIR.
5. Bailey, C., Peddie, D., Wickham, M. E., Badke, K., Small, S. S., Doyle-Waters, M. M., et al. (2016). Adverse drug event reporting systems: A systematic review. British Journal of Clinical Pharmacology, 82(1), 17–29.