Zilverling
Guest staff

prof.dr. P.J.F. Lucas MD (Peter)

Professor Emeritus
EEMCS-CS-DMB
Zilverling 4074
p.j.f.lucas@utwente.nl
+31534899297
Business card

Peter Lucas currently works at the Data Science Department, EEMCS Faculty of the University of Twente. Until the end of 2018 he worked also at the Institute for Computing and Information Sciences at Radboud University Nijmegen, where he still can be found occasionally as a guest.

Expertise

  • Computer Science

    • Bayesian Networks
    • Models
    • Decision Support System
    • Computer Aided Detection
    • Probability

  • Medicine and Dentistry

    • Patient
    • Breast Cancer
    • Disease

Organisations

More than 40 years of experience as an AI researcher in areas such as intelligent systems, computer-based reasoning, decision support systems, model-based reasoning and diagnosis, Bayesian networks, machine learning, eHealth. At the moment most of my research is on probabilistic graphical models, statistical machine learning, and clinical statistical model building. I also have an interest in model-based reasoning and probabilistic logic also applied to other areas than medicine.

Publications

2025

ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment (2025)European journal of cancer, 231. Article 116058. Lombaers, M. S., Reijnen, C., Sprik, A., Bretová, P., Grube, M., Vrede, S., Berg, H. F., Asberger, J., Colas, E., Hausnerova, J., Huvila, J., Gil-Moreno, A., Matias-Guiu, X., Simons, M., Snijders, M. P. L. M., Visser, N. C. M., Kommoss, S., Weinberger, V., Amant, F., … Pijnenborg, J. M. A.https://doi.org/10.1016/j.ejca.2025.116058Explainable automated wild-orchid identification combining deep neural networks and Bayesian networks (2025)Engineering applications of artificial intelligence, 161. Article 111961. Apriyanti, D. H., Spreeuwers, L. J. & Lucas, P. J. F.https://doi.org/10.1016/j.engappai.2025.111961Federated causal discovery with missing data in a multicentric study on endometrial cancer (2025)Journal of biomedical informatics, 169. Article 104877. Zanga, A., Bernasconi, A., Lucas, P. J. F., Pijnenborg, H., Reijnen, C., Scutari, M. & Constantinou, A. C.https://doi.org/10.1016/j.jbi.2025.104877Towards explainable orchid flower identification (2025)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Apriyanti, D. H.https://doi.org/10.3990/1.9789036565684

2024

From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer (2024)Cancers, 16(21). Article 3643. Bernasconi, A., Zanga, A., Lucas, P. J. F., Scutari, M., Di Cosimo, S., De Santis, M. C., La Rocca, E., Baili, P., Cavallo, I., Verderio, P., Ciniselli, C. M., Pizzamiglio, S., Blanda, A., Perego, P., Vallerio, P., Stella, F. & Trama, A.https://doi.org/10.3390/cancers16213643A causal network model to estimate the cardiotoxic effect of oncological treatments in young breast cancer survivors (2024)Progress in Artificial Intelligence (E-pub ahead of print/First online). Bernasconi, A., Zanga, A., Lucas, P. J. F., Scutari, M., Trama, A. & Stella, F.https://doi.org/10.1007/s13748-024-00348-7Introduction to the special issue on IEEE CBMS 2022 mining healthcare: AI and machine learning for biomedicine (2024)Artificial intelligence in medicine, 156. Article 102954. Sicilia, R., Shen, L., Rodríguez-González, A., Santosh, K. C. & Lucas, P. J. F.https://doi.org/10.1016/j.artmed.2024.102954Probabilistic Inference Using Partitioned Bayesian Networks: Introducing a Compositional Framework (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Dal, G.https://doi.org/10.3990/1.9789036559744Model-based Probabilistic Diagnosis in Large Cyberphysical Systems (2024)In Proceedings of the 8th European Conference of the Prognostics and Health Management Society (PHME 2024) (pp. 643-654). PHM Society. Dal, G., Hommersom, A., Grievink, G. & Lucas, P. J. F.https://doi.org/10.36001/phme.2024.v8i1.4033

2023

Deep neural networks for explainable feature extraction in orchid identification (2023)Applied intelligence, 53(21), 26270-26285. Apriyanti, D. H., Spreeuwers, L. J. & Lucas, P. J. F.https://doi.org/10.1007/s10489-023-04880-2

Research profiles

Involved in supervision of students with an interest in AI, and in particular Bayesian networks and knowledge representation an reasoning.

Courses academic year 2025/2026

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2024/2025

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