Publications
2024
Scholarly publications
Grotenhuis, Z., Mosteiro Romero, P., & Leeuwenberg, A. J. M. (Accepted/In press). Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development. Computers in Biology and Medicine, 170, Article 108014. https://doi.org/10.1016/j.compbiomed.2024.108014
2023
Scholarly publications
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. https://doi.org/10.1007/978-3-031-36678-9_5
Ito, T., Fang, Q., Mosteiro Romero, P., Gatt, A., & van Deemter, K. (2023). Challenges in Reproducing Human Evaluation Results for Role-Oriented Dialogue Summarization. In The 3rd Workshop on Human Evaluation of NLP Systems (HumEval’23) ACL Anthology. https://aclanthology.org/2023.humeval-1.9
Belz, A., Thomson, C., Reiter, E., Abercrombie, G., Alonso-Moral, J. M., Arvan, M., Cheung, J., Cieliebak, M., Clark, E., Deemter, K. V., Dinkar, T., Dušek, O., Eger, S., Fang, Q., Gatt, A., Gkatzia, D., González-Corbelle, J., Hovy, D., Hürlimann, M., ... Yang, D. (2023). Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP. In The Fourth Workshop on Insights from Negative Results in NLP (pp. 1-10). Association for Computational Linguistics. https://aclanthology.org/2023.insights-1.1
Rijcken, E., Scheepers, F., Zervanou, K., Spruit, M., Mosteiro Romero, P., & Kaymak, U. (2023). Towards Interpreting Topic Models with ChatGPT. In The 20th World Congress of the International Fuzzy Systems Association (IFSA 2023) (pp. 269-275). International Fuzzy Systems Association.
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 – Exploiting the Value in Data for Health and Innovation (pp. 815 - 816). (Studies in Health Technology and Informatics; Vol. 302). IOS Press. https://doi.org/10.3233/SHTI230273
2022
Scholarly publications
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. https://doi.org/10.1109/SMC53654.2022.9945594
Sarhan, I., Mosteiro Romero, P., & Spruit, M. (2022). UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 271-281). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.semeval-1.35
Mosteiro Romero, P., Kuiper, J., Masthoff, J., Scheepers, F. E., & Spruit, M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), 1-15. Article 237. https://doi.org/10.3390/info13050237
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. https://doi.org/10.3389/fdata.2022.846930
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. https://doi.org/10.1016/j.eswa.2022.116720
2021
Scholarly publications
Rijcken, E., Scheepers, F. E., Mosteiro Romero, P., Zervanou, K., Spruit, M., & Kaymak, U. (2021). A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (SSCI 2021) (pp. 1-8). IEEE. https://doi.org/10.1109/SSCI50451.2021.9660139
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. https://doi.org/10.2991/jaims.d.210225.001
2020
Scholarly publications
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 20–23, 2020, Proceedings (pp. 3-14). (Lecture Notes in Computer Science; Vol. 12435). Springer Cham. https://doi.org/10.1007/978-3-030-61951-0_1