Arjen Hommersom

2024

Evaluating the Usefulness of Counterfactual Explanations from Bayesian Networks

Butz, R., Hommersom, A., Schulz, R. & van Ditmarsch, H., Jun 2024, In: Human-Centric Intelligent Systems. 4, 2, p. 286-298 13 p.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

Letter to the Editor: Nodal infiltration in endometrial cancer: a prediction model using best subset regression

Lombaers, M. S., Haldorsen, I. S., C., R., Hommersom, A. J. & Pijnenborg, J. M. A., Dec 2024, In: European Radiology. 34, 12, p. 7693-7695 3 p.

Research output: Contribution to journal › Comment/Letter to the editor › Academic › peer-review

Machine learning for antidepressant treatment selection in depression

Arnold, P. I. M., Janzing, J. G. E. & Hommersom, A., Aug 2024, In: Drug Discovery Today. 29, 8, 9 p., 104068.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

Model-based Probabilistic Diagnosis in Large Cyberphysical Systems

Lucas, P. J. F., Dal, G. H., Hommersom, A. J. & Grievink, G., 27 Jun 2024, Proceedings of the European Conference of the PHM Society 2024. Do, P. & Ezhilarasu, C. (eds.). 1 ed. PHM Society, Vol. 8. p. 643-654 12 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

2023

GLICE: Combining Graph Neural Networks and Program Slicing to Improve Software Vulnerability Detection

Kraker, W. D., Vranken, H. & Hommmersom, A., 2023, 8th IEEE European Symposium on Security and Privacy Workshops. Institute of Electrical and Electronics Engineers Inc., p. 34-41 8 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Hengst, T. M., Lechner, L., van der Laan, L. N., Hommersom, A., Dohmen, D., Hooft, L., Metting, E., Ebbers, W. & Bolman, C. A. W., 2023, In: JMIR Formative Research. 7, 15 p., e41479.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

2022

Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model

Tummers, S. C. M. W., Hommersom, A., Lechner, L., Bemelmans, R. & Bolman, C. A. W., 19 Dec 2022, In: International Journal of Behavioral Nutrition and Physical Activity. 19, 1, 155.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

Investigating the understandability of XAI methods for enhanced user experience: When Bayesian network users became detectives

Butz, R., Schulz, R., Hommersom, A. & van Eekelen, M., Dec 2022, In: Artificial Intelligence in Medicine. 134, 12 p., 102438.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

One counterfactual does not make an explanation

Butz, R., Hommersom, A., Barenkamp, M. & van Ditmarsch, H., 2022, p. 1-11.

Research output: Contribution to conference › Paper › Academic

Open Access

Robust Identification of Determinants of Physical Activity Behaviour Change

Hommersom, A. J., Tummers, S. C. M. W., Bolman, C. A. W., Bemelmans, R. & Lechner, E. H. S., Aug 2022.

Research output: Contribution to conference › Abstract › Academic

Open Access

2021

A compositional approach to probabilistic knowledge compilation

Dal, G. H., Laarman, A. W., Hommersom, A. & Lucas, P. J. F., Nov 2021, In: International Journal of Approximate Reasoning. 138, p. 38-66 29 p.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

1 Citation (Web of Science)

Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning

Tummers, S. C. M. W., Hommersom, A., Lechner, L., Bolman, C. & Bemelmans, R., 20 May 2021, Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020, Revised Selected Papers. Baratchi, M., Cao, L., Kosters, W. A., Lijffijt, J., van Rijn, J. N. & Takes, F. W. (eds.). Springer Science and Business Media Deutschland GmbH, p. 172-187 16 p. (Communications in Computer and Information Science, Vol. 1398 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

What is understandable in Bayesian network explanations?

Butz, R. S., Schulz, R., Hommersom, A. J. & van Eekelen, M. C. J. D., Oct 2021.

Research output: Contribution to conference › Paper › Academic

Open Access

2020

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Tummers, S. C. M. W., Hommersom, A. J., Lechner, E. H. S., Bolman, C. & Bemelmans, R., 2020, BNAIC/BeneLearn 2020 : Proceedings. Cao, L., Kosters, W. & Lijffijt, J. (eds.). Leiden: Leiden University, p. 298- 312 15 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

File

2632 Downloads (Pure)

Temporal Exceptional Model Mining Using Dynamic Bayesian Networks

Bueno, M. L. P., Hommersom, A. J. & Lucas, P. J. F., 2020, Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020. Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R. & Ifrim, G. (eds.). Springer International Publishing AG, p. 97-112 16 p. (Lecture Notes in Computer Science (LNCS) series).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

2019

A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity

Liu, M., Stella, F., Hommersom, A., Lucas, P. J. F., Boer, L. & Bischoff, E., Apr 2019, In: Artificial Intelligence in Medicine. 95, p. 104-117 14 p.

Research output: Contribution to journal › Article › Academic › peer-review

9 Citations (Web of Science)

A Data-Driven Exploration of Hypotheses on Disease Dynamics

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Janzing, J., 30 May 2019, Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Riaño, D., Wilk, S. & ten Teije, A. (eds.). Cham: Springer International Publishing AG, p. 170-179 10 p. (Lecture Notes in Computer Science (LNCS), Vol. 11526).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

A probabilistic framework for predicting disease dynamics: A case study of psychotic depression

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Janzing, J., Jul 2019, In: Journal of Biomedical Informatics. 95, 12 p., 103232.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

3 Citations (Web of Science)

2018

CSBN: a hybrid approach for survival time prediction with missing data

Rabinowicz, S., Butz, R., Hommersom, A. J. & Williams, M., 14 Sept 2018.

Research output: Contribution to conference › Paper › Academic

Denial-of-Service Attacks on LoRaWAN

van Es, E., Vranken, H. & Hommersom, A., 27 Aug 2018, ARES 2018 - 13th International Conference on Availability, Reliability and Security. New York, NY, USA: acm, 17

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

File

448 Downloads (Pure)

Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning

Kronjee, J., Hommersom, A. & Vranken, H., 27 Aug 2018, ARES 2018 - 13th International Conference on Availability, Reliability and Security. New York, NY, USA: acm, 10 p. 6

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

File

718 Downloads (Pure)

Explaining the Most Probable Explanation

Butz, R. S., Hommersom, A. & van Eekelen, M., 2018, Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Ciucci, D., Pasi, G. & Vantaggi, B. (eds.). Cham: Springer International Publishing AG, p. 50-63 14 p. (Lecture Notes in Computer Science, Vol. 11142).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Making Continuous Time Bayesian Networks More Flexible

Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F., 1 Nov 2018, Proceedings of the Ninth International Conference on Probabilistic Graphical Models. Kratochvíl, V. & Studený, M. (eds.). Prague, Czech Republic: PMLR, Vol. 72. p. 237-248 12 p. (Proceedings of Machine Learning Research).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

Modeling the Dynamics of Multiple Disease Occurrence by Latent States

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Lobo, M. & Rodrigues, P. P., 2018, Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Ciucci, D., Pasi, G. & Vantaggi, B. (eds.). Cham: Springer International Publishing AG, p. 93-107 15 p. (Lecture Notes in Computer Science; No. 11142).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Probabilistic logic programming (PLP) 2016

Hommersom, A. & Cussens, J., May 2018, In: International Journal of Approximate Reasoning. 96, p. 56-56 1 p.

Research output: Contribution to journal › Editorial › Academic

Open Access

Representing Hypoexponential Distributions in Continuous Time Bayesian Networks

Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F., 2018, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. Medina, J., Ojeda-Aciego, M., Verdegay, J. L., Perfilieva, I., Bouchon-Meunier, B. & Yager, R. R. (eds.). Cham: Springer International Publishing AG, Vol. 855. p. 565-577 13 p. (Communications in Computer and Information Science, Vol. 855).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

2017

A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks

Rabinowicz, S., Hommersom, A., Butz, R. S. & Williams, M., 2017, Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings. ten Teije, A., Popow, C., Holmes, J. H. & Sacchi, L. (eds.). Cham: Springer International Publishing AG, p. 81-85 5 p. (Lecture Notes in Computer Science, Vol. 10259).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Asymmetric hidden Markov models

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Linard, A., Sept 2017, In: International Journal of Approximate Reasoning. 88, p. 169-191 23 p.

Research output: Contribution to journal › Article › Academic › peer-review

7 Citations (Web of Science)

Exploiting Experts' Knowledge for Structure Learning of Bayesian Networks

Amirkhani, H., Rahmati, M., Lucas, P. J. F. & Hommersom, A., Nov 2017, In: Ieee Transactions on Pattern Analysis and Machine Intelligence. 39, 11, p. 2154-2170 17 p.

Research output: Contribution to journal › Article › Academic › peer-review

28 Citations (Web of Science)

Hybrid time Bayesian networks

Liu, M., Hommersom, A., van der Heijden, M. & Lucas, P. J. F., Jan 2017, In: International Journal of Approximate Reasoning. 80, p. 460-474 15 p.

Research output: Contribution to journal › Article › Academic › peer-review

6 Citations (Web of Science)

2016

Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

Michels, S., Hommersom, A. & Lucas, P. J. F., Jul 2016, IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Brewka, G. (ed.). AAAI Press, p. 3616-3622 7 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Verwer, S. & Linard, A., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models: Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 2016. Antonucci, A., Corani, G. & Campos, C. P. (eds.). PMLR, Vol. 52. p. 50-61 12 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

Learning Parameters of Hybrid Time Bayesian Networks

Liu, M., Hommersom, A., Heijden, M. V. D. & Lucas, P. J. F., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models: Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 201. Antonucci, A., Corani, G. & de Campos, C. P. (eds.). PMLR, Vol. 52. p. 287-298 12 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks. : Machine Learning for Health

Bekker, J., Hommersom, A. J., Lappenschaar, M. & Janzing, J. G. E., 9 Dec 2016.

Research output: Contribution to conference › Paper › Academic

Understanding disease processes by partitioned dynamic Bayesian networks

Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Lappenschaar, M. & Janzing, J. G. E., Jun 2016, In: Journal of Biomedical Informatics. 61, p. 283-297 15 p.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

10 Citations (Web of Science)

2015

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Anselma, L., Bottrighi, A., Giordano, L., Hommersom, A. J., Molino, G., Montani, S., Terenziani, P. & Torchio, M., 2015, Foundations of Biomedical Knowledge Representation: Methods and Applications. Hommersom, A. & Lucas, P. (eds.). Cham: Springer International Publishing, p. 287-315 29 p. (Lecture Notes in Computer Science, Vol. 9521). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9521).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

A new probabilistic constraint logic programming language based on a generalised distribution semantics

Michels, S., Hommersom, A., Lucas, P. J. F. & Velikova, M., Nov 2015, In: Artificial Intelligence. 228, p. 1-44 44 p.

Research output: Contribution to journal › Article › Academic › peer-review

4 Citations (Web of Science)

An Introduction to Knowledge Representation and Reasoning in Healthcare

Hommersom, A. J. & Lucas, P. J. F., 2015, Foundations of Biomedical Knowledge Representation: Methods and Applications. Hommersom, A. & Lucas, P. J. F. (eds.). Cham: Springer International Publishing, p. 9-32 24 p. (Lecture Notes in Computer Science (LNCS), Vol. 9521). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9521).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Open Access

Foundations of Biomedical Knowledge Representation: Methods and Applications

Hommersom, A. (Editor) & Lucas, P. J. F. (Editor), 2015, 1 ed. Cham: Springer International Publishing AG. 339 p. (Lecture Notes in Computer Science, Vol. 9521). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9521).

Research output: Book/Report › Book editing › Academic

How to Read the Book “Foundations of Biomedical Knowledge Representation”

Lucas, P. J. F. & Hommersom, A. J., 2015, Foundations of Biomedical Knowledge Representation: Methods and Applications. Hommersom, A. & Lucas, P. (eds.). Cham: Springer International Publishing, p. 3-7 5 p. (Lecture Notes in Computer Science, Vol. 9521).

Research output: Chapter in Book/Report/Conference proceeding › Foreword/postscript › Academic

Hybrid Time Bayesian Networks

Liu, M., Hommersom, A. J., van der Heijden, M. & Lucas, P. J. F., 2015, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Destercke, S. & Denoeux, T. (eds.). Cham: Springer International Publishing AG, p. 376-386 11 p. (Lecture Notes in Computer Science (LNCS) series, Vol. 9161). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9161).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Mining Hierarchical Pathology Data Using Inductive Logic Programming

Op De Beéck, T., Hommersom, A., Van Haaren, J., van der Heijden, M., Davis, J., Lucas, P. J. F., Overbeek, L. & Nagtegaal, I., 2015, Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings. Holmes, J. H., Bellazzi, R., Sacchi, L. & Peek, N. (eds.). Cham: Springer International Publishing AG, p. 76-85 10 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks

Lucas, P. J. F. & Hommersom, A., Mar 2015, In: International Journal of Intelligent Systems. 30, 3, p. 209-235 27 p.

Research output: Contribution to journal › Article › Academic › peer-review

3 Citations (Web of Science)

2014

Qualitative chain graphs and their application

Lappenschaar, M., Hommersom, A. & Lucas, P. J. F., Jun 2014, In: International Journal of Approximate Reasoning. 55, 4, p. 957-976 20 p.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

1 Citation (Web of Science)

Arjen Hommersom

Research output

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