Browsing by Author "Hommersom, A.J."
Now showing items 150 of 80

Temporal Exceptional Model Mining Using Dynamic Bayesian Networks
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.2020, Article in monograph or in proceedings (Lemaire, V. (ed.), Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers, pp. 97112) 
Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning
Tummers, S.; Hommersom, A.J.; Lechner, L.; Bolman, C.; Bemelmans, R.2020, Article in monograph or in proceedings (Cao, L. (ed.), BNAIC/BeneLearn 2020: Proceedings Leiden, the Netherlands November 19–20, 2020, pp. 298312) 
A probabilistic framework for predicting disease dynamics: A case study of psychotic depression
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Janzing, J.G.2019, Article / Letter to editor (Journal of Biomedical Informatics, 95, (2019), article 103232) 
A DataDriven Exploration of Hypotheses on Disease Dynamics
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Janzing, J.2019, Article in monograph or in proceedings (Riano, D. (ed.), Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, pp. 170179) 
A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity
Liu, Manxia; Stella, F.; Hommersom, A.J.; Lucas, Peter J.F.; Boer, L.M.; Bischoff, E.W.2019, Article / Letter to editor (Artificial Intelligence in Medicine, 95, (2019), pp. 104117) 
Modeling the Dynamics of Multiple Disease Occurrence by Latent States
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Lobo, M.; Rodrigues, P.P.2018, Article in monograph or in proceedings (Ciucci, D. (ed.), Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 35, 2018, Proceedings, pp. 93107) 
Representing Hypoexponential Distributions in Continuous Time Bayesian Networks
Liu, M.; Stella, F.; Hommersom, A.J.; Lucas, P.J.F.2018, Part of book or chapter of book (Medina, J. (ed.), Information Processing and Management of Uncertainty in KnowledgeBased Systems. Applications 17th International Conference, IPMU 2018, Cádiz, Spain, June 1115, 2018, Proceedings, Part III, pp. 565577) 
Probabilistic logic programming (PLP) 2016: Editorial
Hommersom, A.J.; Cussens, J.2018, Article / Letter to editor (International Journal of Approximate Reasoning, 96, (2018), pp. 1, article 56) 
Explaining the Most Probable Explanation
Butz, R.; Hommersom, A.J.; Eekelen, M.C.J.D. van2018, Article in monograph or in proceedings (Ciucci, D. (ed.), Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 35, 2018. Proceedings, pp. 5063) 
Discovering software vulnerabilities using dataflow analysis and machine learning
Kronjee, J.; Hommersom, A.J.; Vranken, H.P.E.2018, Article in monograph or in proceedings (ARES 2018: Proceedings of the 13th International Conference on Availability, Reliability and Security, Hamburg, Germany — August 27  30, 2018, pp. 61  610) 
DenialofService Attacks on LoRaWAN
Es, E. van; Vranken, H.P.E.; Hommersom, A.J.2018, Article in monograph or in proceedings (ARES 2018: Proceedings of the 13th International Conference on Availability, Reliability and Secuengrity, Hamburg, Germany — August 27  30, 2018, pp. 16) 
Making Continuous Time Bayesian Networks More Flexible
Liu, M.; Stella, F.; Hommersom, A.J.; Lucas, P.J.F.2018, Article / Letter to editor (Proceedings of Machine Learning Research, 72, (2018), pp. 237248) 
Hybrid time Bayesian networks
Liu, M.; Hommersom, A.J.; Heijden, M. van der; Lucas, P.J.F.2017, Article / Letter to editor (International Journal of Approximate Reasoning, 80, (2017), pp. 460474) 
A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks
Rabinowicz, S.; Hommersom, A.J.; Butz, R.; Williams, M.2017, Article in monograph or in proceedings (Teije, A. ten; Popow, C.; Holmes, J.H. (ed.), Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 2124, 2017, Proceedings, pp. 8185) 
Exploiting Experts' Knowledge for Structure Learning of Bayesian Networks
Amirkhani, H.; Rahmati, M.; Lucas, P.; Hommersom, A.J.2017, Article / Letter to editor (IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 11, (2017), pp. 21542170) 
Asymmetric hidden Markov models
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Linard, A.R.R.2017, Article / Letter to editor (International Journal of Approximate Reasoning, 88, (2017), pp. 169191) 
Understanding disease processes by partitioned dynamic Bayesian networks
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Lappenschaar, G.A.M.; Janzing, J.G.2016, Article / Letter to editor (Journal of Biomedical Informatics, 61, June, (2016), pp. 283297) 
Understanding disease processes by partitioned dynamic Bayesian networks
Bueno, M.L.; Hommersom, A.J.; Lucas, P.J.F.; Lappenschaar, M.; Janzing, J.G.E.2016, Article / Letter to editor (Journal of Biomedical Informatics, 61, (2016), pp. 283297) 
Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming
Michels, S.; Hommersom, A.J.; Lucas, P.J.F.2016, Article in monograph or in proceedings (Kambhampati, S. (ed.), IJCAI 2016 : Proceedings of the TwentyFifth International Joint Conference on Artificial Intelligence New York, New York, USA 9–15 July 2016, pp. 36163622) 
Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case
Bueno, M.L.P.; Hommersom, A.J.; Lucas, P.J.F.; Verwer, S.; Linard, A.R.R.2016, Article in monograph or in proceedings (Antonucci, A. (ed.), PGM 2016 : Proceedings of the Eighth International Conference on Probabilistic Graphical Models, Lugano, 69 September 2016, pp. 5061) 
Learning Parameters of Hybrid Time Bayesian Networks
Liu, M.; Hommersom, A.J.; Heijden, M. van der; Lucas, P.J.F.2016, Article in monograph or in proceedings (Antonucci, A. (ed.), PGM 2016 : Proceedings of the Eighth International Conference on Probabilistic Graphical Models, Lugano, 69 September 2016, pp. 287298) 
Toward Computing ConflictBased Diagnoses in Probabilistic Logic Programming
Hommersom, A.J.; Bueno, M.L.P.2016, Article in monograph or in proceedings (Hommersom, A. (ed.), PLP 2016 : Probabilistic Logic Programming Proceedings of the 3rd International Workshop on Probabilistic Logic Programming colocated with 26th International Conference on Inductive Logic Programming (ILP 2016) London, UK, September 3, 2016, pp. 2938) 
An Introduction to Knowledge Representation and Reasoning in Healthcare
Hommersom, A.J.; Lucas, P.J.F.2015, Part of book or chapter of book (Hommersom, A.; Lucas, J.F.P. (ed.), Foundations of Biomedical Knowledge Representation: Methods and Applications, pp. 932) 
Hybrid Time Bayesian Networks
Liu, M.; Hommersom, A.J.; Heijden, M. van der; Lucas, P.J.F.2015, Part of book or chapter of book (Destercke, S.; Denoeux, T. (ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 376386) 
Foundations of Biomedical Knowledge Representation : Methods and Applications
Hommersom, A.J.; Lucas, P.J.F.2015, Book (monograph) 
A new probabilistic constraint logic programming language based on a generalised distribution semantics
Michels, S.; Hommersom, A.J.; Lucas, P.J.F.; Velikova, M.V.2015, Article / Letter to editor (Artificial Intelligence, 228, November, (2015), pp. 144) 
How to Read the Book “Foundations of Biomedical Knowledge Representation”
Lucas, P.J.F.; Hommersom, A.J.2015, Part of book or chapter of book (Hommersom, A.; Lucas, J.F.P. (ed.), Foundations of Biomedical Knowledge Representation: Methods and Applications, pp. 37) 
Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks
Lucas, P.J.F.; Hommersom, A.J.2015, Article / Letter to editor (International Journal of Intelligent Systems, 30, 3, (2015), pp. 209235) 
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, Part of book or chapter of book (Hommersom, A.; Lucas, J.F.P. (ed.), Foundations of Biomedical Knowledge Representation: Methods and Applications, pp. 287315) 
Mining Hierarchical Pathology Data Using Inductive Logic Programming
Beéck, T. Op De; Hommersom, A.J.; Haaren, J. Van; Heijden, M. van der; Davis, J; Lucas, P.; Overbeek, L.; Nagtegaal, I.2015, Part of book or chapter of book (Holmes, J.H.; Bellazzi, R.; Sacchi, L. (ed.), Artificial Intelligence in Medicine, pp. 7685) 
Supporting Physicians and Patients Through Recommendation: Guidelines and Beyond
Anselma, L.; Bottrighi, A.; Hommersom, A.J.; Terenziani, P.; Hunter, A.2015, Part of book or chapter of book (Hommersom, A.; Lucas, J.F.P. (ed.), Foundations of Biomedical Knowledge Representation: Methods and Applications, pp. 281286) 
Qualitative chain graphs and their application
Lappenschaar, G.A.M.; Hommersom, A.J.; Lucas, P.J.F.2014, Article / Letter to editor (International Journal of Approximate Reasoning, 55, 4, (2014), pp. 957976) 
Imprecise Probabilistic Horn Clause Logic
Michels, S.; Hommersom, A.J.; Lucas, P.J.F.; Velikova, M.V.2014, Part of book or chapter of book (Schaub, T. (ed.), ECAI 2014 : 21st European Conference on Artificial Intelligence, 1822 August 2014, Prague, Czech Republic. Includi Proceedings, pp. 621626) 
Causal Independence Models for Continuous Time Bayesian Networks
Heijden, M. van der; Hommersom, A.J.2014, Part of book or chapter of book (Gaag, L.C. van der; Feelders, A. (ed.), Probabilistic Graphical Models : 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 1719, 2014. Proceedings, pp. 503518) 
Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity
Lappenschaar, M.; Hommersom, A.J.; Lucas, P.; Lagro, J.; Visscher, S.; Korevaar, J.C.; Schellevis, F.G.2013, Article / Letter to editor (Journal of Clinical Epidemiology, 66, 12, (2013), pp. 14051416) 
Moshca – my mobile and smart health care assistant
Hommersom, A.J.; Lucas, P.J.F.; Velikova, M.V.; Dal, G.; Bastos, J.; Rodriguez, J.; Germs, M.; Schwietert, H.2013, Article in monograph or in proceedings (Healthcom'13 : 15th international conference on Ehealth networking, applications & services, October 912, 2013, Lisbon, Portugal, pp. 188192) 
Inference for a New Probabilistic Constraint Logic
Michels, S.; Hommersom, A.J.; Lucas, P.J.F.; Velikova, M.V.; Koopman, P.2013, Article in monograph or in proceedings (Rossi, F. (ed.), IJCAI13: Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence Beijing, China, 3–9 August 2013, pp. 25402543) 
Probabilistic problem solving in biomedicine
Hommersom, A.J.; Lucas, P.J.F.2013, Article / Letter to editor (Artificial Intelligence in Medicine, 57, 3, (2013), pp. 169170) 
Understanding the Cooccurrence of Diseases Using Structure Learning
Lappenschaar, G.A.M.; Hommersom, A.J.; Lagro, J.; Lucas, P.J.F.2013, Part of book or chapter of book (Peek, N.; Morales, R. Marín; Peleg, M. (ed.), Artificial Intelligence in Medicine, pp. 135144) 
A decision support model for uncertainty reasoning in safety and security tasks
Michels, S.; Velikova, M.V.; Hommersom, A.J.; Lucas, P.J.F.2013, Article in monograph or in proceedings (Lai, L. Lei (ed.), 2013 IEEE International Conference on Systems, Man, and Cybernetics SMC 2013 : Proceedings, pp. 663668) 
Multilevel Bayesian networks for the analysis of hierarchical health care data
Lappenschaar, G.A.M.; Hommersom, A.J.; Lucas, P.J.F.; Lagro, J.; Visscher, S.2013, Article / Letter to editor (Artificial Intelligence in Medicine, 57, 3, (2013), pp. 171183) 
Reasoning with uncertainty about system behaviour: Making printing systems adaptive
Evers, S.; Hommersom, A.J.; Lucas, P.; Cochior, C.; Bosch, P.2013, Part of book or chapter of book (Basten, T.; Hamberg, R.; Reckers, F. (ed.), Modelbased design of adaptive embedded systems, pp. 125158) 
Exploring Disease Interactions Using Markov Networks
Haaren, J. Van; Davis, J; Lappenschaar, G.A.M.; Hommersom, A.J.2013, Article in monograph or in proceedings (AAAI13 : Workshops at the TwentySeventh AAAI Conference on Artificial Intelligence Hyatt Regency Bellevue July 14, 2013 – July 15, 2013, pp. 6570) 
Multilevel Bayesian networks for the analysis of hierarchical health care data
Lappenschaar, M.; Hommersom, A.J.; Lucas, P.J.F.; Lagro, J.; Visscher, S.2013, Article / Letter to editor (Artificial Intelligence in Medicine, 57, 3, (2013), pp. 171183) 
Discovering Probabilistic Structures of Healthcare Processes
Hommersom, A.J.; Verwer, S.; Lucas, P.J.F.2013, Part of book or chapter of book (Riaño, D.; Lenz, R.; Miksch, S. (ed.), Process Support and Knowledge Representation in Health Care: AIME 2013 Joint Workshop, KR4HC 2013/ProHealth 2013, Murcia, Spain, June 1, 2013, Revised Selected Papers, pp. 5367) 
Discovering probabilistic structures of healthcare processes
Hommersom, A.J.; Verwer, S.E.; Lucas, P.J.F.2013, Article in monograph or in proceedings (Riaño, D.; Lenz, R.; Miksch, S. (ed.), Process Support and Knowledge Representation in Health Care : AIME 2013 Joint Workshop, KR4HC 2013/ProHealth 2013, Murcia, Spain, June 1, 2013, Revised Selected Papers, pp. 5367) 
Qualitative chain graphs and their use in medicine
Lappenschaar, G.A.M.; Hommersom, A.J.; Lucas, P.J.F.2012, Article in monograph or in proceedings (Cano, A.; GómezOlmedo, M.; Nielsen, T.D. (ed.), PGM 2012 : Proceedings of the Sixth European Workshop on Probabilistic Graphical Models, PGM'12 Granada, Spain September 1921, 2012, pp. 179186) 
A SemiCausal Bayesian Network Approach to Prognosis
Hommersom, A.J.; Altena, A. van; Lucas, P.J.F.2012, Article in monograph or in proceedings (Langford, J. (ed.), ICML 2012 : Proceedings of the 29th International Conference on Machine Learning, Edingburgh, Scotland, June 26July 1, 2012, pp. 18) 
Probabilistic Causal Models of Multimorbidity Concepts
Lappenschaar, G.A.M.; Hommersom, A.J.; Lucas, P.J.F.2012, Article in monograph or in proceedings (AMIA Proceedings of the 2012 Annual Symposium, pp. 475484)