The Dutch Clinical NLP Workshop


The 3rd Dutch Clinical NLP workshop is organized by a team from Utrecht University’s Natural Language and Text Processing (NLTP) group and VeiligheidNL.

and Kiki Houwers who among other things has helped design this website (but has to date not provided us with a photo ūüôÉ)

The NLTP group works together on research projects  to understand, interpret and model natural language. In addition, the group develops a wide range of open educational materials on text mining and NLP, plus many tools for the community to use.

VeiligheidNL is the centre of expertise for injury prevention and has been working for 40 years to make the lives of millions of people safer by promoting safe behavior in a safe environment. The centre aims to achieve this through monitoring, expertise promotion, educating, and advising partners and professionals.

Some of our recent publications on the application of NLP in medical contexts include:

Bagheri, A., Giachanou, A., Mosteiro Romero, P., & Verberne, S. (2023). Natural Language Processing and Text Mining (Turning Unstructured Data into Structured). In F. Asselbergs, S. Denaxas, D. Oberski, & J. Moore (Eds.), Clinical Applications of Artificial Intelligence in Real-World Data (1 ed., pp. 69-93). Springer.

van Buchem, M. M., ‘t Hart, H., Mosteiro Romero, P., Kant, I. M. J., & Bauer, M. P. (2023).¬†Diagnosis Classification in the Emergency Room Using Natural Language Processing. In¬†Caring is Sharing ‚Äď Exploiting the Value in Data for Health and Innovation¬†(pp. 815 – 816). (Studies in Health Technology and Informatics; Vol. 302). IOS Press.¬†

Borger, T., Mosteiro Romero, P., Kaya, H., Rijcken, E., Salah, A., Scheepers, F., & Spruit, M.(2022). Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting. Expert Systems with Applications, 199, 1-9. Article 116720.

Rijcken, E., Kaymak, U., Scheepers, F. E., Mosteiro Romero, P., Zervanou, K., & Spruit, M. (2022). Topic Modeling for Interpretable Text Classification From EHRs. Frontiers in Big Data, 5, 1-11. Article 846930.

Rijcken, E., Zervanou, K., Spruit, M., Mosteiro Romero, P., Scheepers, F. E., & Kaymak, U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. In IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669-2674). IEEE.

Teijema, J., Hofstee, L., Brouwer, M., de Bruin, J., Ferdinands, G., de Boer, J., Siso, P. V., van den Brand, S., Bockting, C., van de Schoot, R., & others. (2022). Active learning-based Systematic reviewing using switching classification models: The case of the onset, maintenance, and relapse of depressive disorders.

Bagheri, A. (2021). Text mining in healthcare: Bringing structure to electronic health records. Utrecht University.


Bagheri, A., Groenhof, T. K. J., Asselbergs, F. W., Haitjema, S., Bots, M. L., Veldhuis, W. B., De Jong, P. A., & Oberski, D. L. (2021). Automatic prediction of recurrence of major cardiovascular events: A text mining study using chest X-ray reports. Journal of Healthcare Engineering, 2021.


Mosteiro Romero, P., Rijcken, E., Zervanou, K., Kaymak, U., Scheepers, F. E., & Spruit, M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2( 1-2), 44-54.

Mosteiro Romero, P. J., Rijcken, E., Zervanou, K., Kaymak, U., Scheepers, F., & Spruit, M. (2020). Making sense of violence risk predictions using clinical notes. In Z. Huang, S. Siuly, H. Wang, R. Zhou, & Y. Zhang (Eds.), Health Information Science: 9th International Conference, HIS 2020, Amsterdam, The Netherlands, October 20‚Äď23, 2020, Proceedings (pp. 3-14). (Lecture Notes in Computer Science; Vol. 12435). Springer Cham.