Publications

A complete list of my publications can be found on my Google Scholar or Ghent University page.

Below is a list of selected papers on my various research topics.

Machine learning for healthcare

• De Brouwer, M., Vandenbussche, N., Steenwinckel, B., Stojchevska, M., Van Der Donckt, J., Degraeve, V., Vaneessen, J., De Turck, F., Volckaert, B., Boon, P., Paemeleire, K., Van Hoecke, S., Ongenae, F. (2022). mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients. BMC MEDICAL INFORMATICS AND DECISION MAKING, 1, 1-34. https://link.springer.com/article/10.1186/s12911-022-01813-w

• Vandewiele, G., Dehaene, I., Kovács, G., Sterckx, L., Janssens, O., Ongenae, F., … Demeester, T. (2021). Overly optimistic prediction results on imbalanced data : a case study of flaws and benefits when applying over-sampling. ARTIFICIAL INTELLIGENCE IN MEDICINE, 111. http://hdl.handle.net/1854/LU-8692222

• Houthooft, R., Ruyssinck, J., van der Herten, J., Stijven, S., Couckuyt, I., Gadeyne, B., Ongenae, F., et al. (2015). Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. ARTIFICIAL INTELLIGENCE IN MEDICINE, 63(3), 191–207. http://hdl.handle.net/1854/LU-5971162

• Ongenae, F., Van Looy, S., Verstraeten, D., Verplancke, T., Benoit, D., De Turck, F., Dhaene, T., et al. (2013). Time series classification for the prediction of dialysis in critically ill patients using echo state networks. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 26(3), 984–996. http://hdl.handle.net/1854/LU-3260114

• Ruyssinck, J., van der Herten, J., Houthooft, R., Ongenae, F., Couckuyt, I., Gadeyne, B., Colpaert, K., et al. (2016). Random survival forests for predicting the bed occupancy in the intensive care unit. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE. http://hdl.handle.net/1854/LU-8507757

• Janssens, O., Smets, E., Schavione, G., Rios Velazquez, E., Ongenae, F., De Raedt, W., Van Hoof, C., et al. (2018). Context-aware stress detection (pp. 1–1). Presented at the BHI2018, the IEEE Biomedical and Health Informatics Conference. http://hdl.handle.net/1854/LU-8559989

• Van Steenkiste, T., Ruyssinck, J., De Baets, L., Decruyenaere, J., De Turck, F., Ongenae, F., & Dhaene, T. (2019). Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. ARTIFICIAL INTELLIGENCE IN MEDICINE, 97, 38–43. http://hdl.handle.net/1854/LU-8585847

Semantic healthcare decision support systems

• Ongenae, F., Claeys, M., Dupont, T., Kerckhove, W., Verhoeve, P., Dhaene, T., & De Turck, F. (2013). A probabilistic ontology-based platform for self-learning context-aware healthcare applications. EXPERT SYSTEMS WITH APPLICATIONS, 40(18), 7629–7646. http://hdl.handle.net/1854/LU-4191983

• Ongenae, F., Myny, D., Dhaene, T., Defloor, T., Van Goubergen, D., Verhoeve, P., Decruyenaere, J., et al. (2011). An ontology-based nurse call management system (oNCS) with probabilistic priority assessment. BMC HEALTH SERVICES RESEARCH, 11. http://hdl.handle.net/1854/LU-1196048

• De Backere, F., Bonte, P., Verstichel, S., Ongenae, F., & De Turck, F. (2017). The OCarePlatform : a context-aware system to support independent living. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 140, 111–120. http://hdl.handle.net/1854/LU-8541182

• De Backere, F., Ongenae, F., Van den Abeele, F., Nelis, J., Bonte, P., Clement, E., Philpott, M., et al. (2015). Towards a social and context-aware multi-sensor fall detection and risk assessment platform. COMPUTERS IN BIOLOGY AND MEDICINE, 64, 307–320. http://hdl.handle.net/1854/LU-7235308

• Vandewiele, G., De Backere, F., Lannoye, K., Vanden Berghe, M., Janssens, O., Van Hoecke, S., Keereman, V., et al. (2018). A decision support system to follow up and diagnose primary headache patients using semantically enriched data. BMC MEDICAL INFORMATICS AND DECISION MAKING, 18. http://hdl.handle.net/1854/LU-8583115

• Mahieu, C., Ongenae, F., De Backere, F., Bonte, P., De Turck, F., & Simoens, P. (2019). Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things. JOURNAL OF SYSTEMS AND SOFTWARE, 149, 138–157. http://hdl.handle.net/1854/LU-8603541

Stream reasoning

• Tommasini, R., Bonte, P., Ongenae, F., Della Valle, E. (2021). RSP4J: An API for RDF Stream Processing. In Proc. of the European Semantic Web Conference (best resource paper award), 555-581. https://link.springer.com/chapter/10.1007/978-3-030-77385-4_34

• Bonte, P., Tommasini, R., Della Valle, E., De Turck, F., & Ongenae, F. (2018). Streaming MASSIF : cascading reasoning for efficient processing of IoT data streams. SENSORS, 18(11). http://hdl.handle.net/1854/LU-8586329

• Bonte, P., Tommasini, R., De Turck, F., Ongenae, F., & Valle, E. D. (2019). C-Sprite : efficient hierarchical reasoning for rapid RDF stream processing. In DEBS ’19 : Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems (pp. 103–114). Darmstadt, Germany: ACM. http://hdl.handle.net/1854/LU-8635009

• De Brouwer, Mathias, Ongenae, F., Bonte, P., & De Turck, F. (2018). Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions. SENSORS, 18(10). http://hdl.handle.net/1854/LU-8581923

Hybrid AI (fusing semantics & ML) & Knowledge Graph embedding

• Steenwinckel, B., Vandewiele, G., Weyns, M., Agozzino, T., De Turck, F., Ongenae, F. (2022). INK: Knowledge graph embeddings for node classification. 36(2), 620-667. https://link.springer.com/article/10.1007/s10618-021-00806-z

• Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., … Ongenae, F. (2021). FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems, Vol. 116, pp. 30-48. http://hdl.handle.net/1854/LU-8686257

• Vandewiele, G., Steenwinckel, B., De Turck, F., Ongenae, F. (2020). MINDWALC: Mining Interpretable, Discriminative Walks for Classication of Nodes in a Knowledge Graph. BMC Medical Informatics and Decision Making. vol. 20, no. Suppl 4. http://hdl.handle.net/1854/LU-8686262

• Vandewiele, G., Steenwinckel, B., Bonte, P., Weyns, M., Paulheim, H., Ristoski, P., De Turck, F., Ongenae, F. (2020). Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs. https://arxiv.org/abs/2009.04404

• Weyns, M., Bonte, P., Steenwinckel, B., De Turck, F., & Ongenae, F. (2020). Conditional constraints for knowledge graph embeddings. In M. Alam, D. Buscaldi, M. Cochez, F. Osborne, R. Reforgiato, & H. Diego and Sack (Eds.), Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2020) co-located with the 17th Extended Semantic Web Conference 2020 (ESWC 2020) (Vol. 2635). online. http://hdl.handle.net/1854/LU-8675058

Explainable / Interpretable AI

• Vander Mijnsbrugge, D., Ongenae, F., Van Hoecke, S. (2021). Parameter Efficient Neural Networks With Singular Value Decomposed Kernels. IEEE Transactions on Neural Networks an Learning Systems. https://ieeexplore.ieee.org/abstract/document/9662281

• Vandewiele, G., Lannoye, K., Janssens, O., Ongenae, F., De Turck, F., & Van Hoecke, S. (2017). A genetic algorithm for interpretable model extraction from decision tree ensembles. In U. Kang, E.-P. Lim, J. X. Yu, & Y.-S. Moon (Eds.), Trends and applications in knowledge discovery and data mining, 2017 (Vol. 10526, pp. 104–115). Presented at the 21st Pacific-Asia conference on Knowledge Discovery and Data Mining (PAKDD 2017), Cham, Switzerland: Springer. http://hdl.handle.net/1854/LU-8537061

• Vandewiele, G., Ongenae, F., De Turck, F. (2021). GENDIS: GENetic DIscovery of Shapelets. SENSORS, vol. 21, no. 4. http://hdl.handle.net/1854/LU-8700414

• Bonte, P., Ongenae, F., & De Turck, F. (2019). Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 29(6), 819–847. http://hdl.handle.net/1854/LU-8623325

Semantic reasoning

• Bonte, P., Ongenae, F., De Backere, F., Schaballie, J., Arndt, D., Verstichel, S., Mannens, E., et al. (2017). The MASSIF platform : a modular and semantic platform for the development of flexible IoT services. KNOWLEDGE AND INFORMATION SYSTEMS, 51(1), 89–126. ttp://hdl.handle.net/1854/LU-8533438

• Verstichel, S., Ongenae, F., Loeve, L., Vermeulen, F., Dings, P., Dhoedt, B., Dhaene, T., et al. (2011). Efficient data integration in the railway domain through an ontology-based methodology. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 19(4), 617–643. http://hdl.handle.net/1854/LU-1268227

• Bonte, P., Ongenae, F., & De Turck, F. (2019). Subset reasoning for event-based systems. IEEE ACCESS, 7, 107533–107549. http://hdl.handle.net/1854/LU-8627930

Predictive maintenance (anomaly detection, root cause analysis)

• Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., … Ongenae, F. (2021). FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems, Vol. 116, pp. 30-48. http://hdl.handle.net/1854/LU-8686257

• Steenwinckel, B., Heyvaert, P., De Paepe, D., Janssens, O., Vanden Hautte, S., Dimou, A., De Turck, F., et al. (2018). Towards adaptive anomaly detection and root cause analysis by automated extraction of knowledge from risk analyses. Proceedings of the 9th international semantic sensor networks workshop, co-located with 17th international semantic web conference (ISWC 2018) (Vol. 2213, pp. 17–31). Presented at the 9th International Semantic Sensor Networks workshop, co-located with 17th International Semantic Web conference (ISWC 2018). http://hdl.handle.net/1854/LU-8583136

(Ontology) Co-design with domain experts

• Ongenae, F., Bleumes, L., Sulmon, N., Verstraete, M., Van Gils, M., Jacobs, A., De Zutter, S., et al. (2011). Participatory design of a continuous care ontology : towards a user-driven ontology engineering methodology. In J Filipe & J. Dietz (Eds.), KEOD 2011 : proceedings of the international conference on knowledge engineering and ontology development (pp. 81–90). Presented at the International conference on Knowledge Engineering and Ontology Development (KEOD 2011), Setubal, Portugal: INSTICC. http://hdl.handle.net/1854/LU-1965779

• Ongenae, F., Duysburgh, P., Sulmon, N., Verstraete, M., Bleumers, L., De Zutter, S., Verstichel, S., et al. (2014). An ontology co-design method for the co-creation of a continuous care ontology. APPLIED ONTOLOGY, 9(1), 27–64. http://hdl.handle.net/1854/LU-5730943

• Jacobs, A, Duysburgh, P., Bleumers, L., Ongenae, F., Ackaert, A., & Verstichel, S. (2014). The innovation binder approach: a guide towards a social-technical balanced pervasive health system. Pervasive health : state-of-the art and beyond (pp. 69–99). Springer. http://hdl.handle.net/1854/LU-5730882

• Seymoens, T., Ongenae, F., Jacobs, A., Verstichel, S., & Ackaert, A. (2019). A methodology to involve domain experts and machine learning techniques in the design of human-centered algorithms. In Human work interaction design : designing engaging automation (pp. 200–214). Cham: Springer. ttp://hdl.handle.net/1854/LU-8606686

• Ongenae, F., Duysburgh, P., Verstraete, M., Sulmon, N., Bleumers, L., Jacobs, A., Ackaert, A., et al. (2012). User-driven design of a context-aware application: an ambient-intelligent nurse call system. 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (pp. 205–210). Presented at the 2012 6th International Conference on Pervasive Computing Technologies for Healthcare, Piscataway, NJ, USA: IEEE. http://hdl.handle.net/1854/LU-2983798

PhD Dissertation

Ongenae, F. (2013). Ontology design and management for eCare services. Department of Information technology, Ghent, Belgium. https://biblio.ugent.be/publication/4133415

Media

• De Nieuwe Generatie AI modellen in de Geneeskunde: Slim en Transparant, EOS Wetenschap, Special Technologie en Gezondheid, 2020. (Dutch)

• Altijd de juiste dosis antibiotica, EOS Wetenschap, Special Technologie en Gezondheid, 2020. (Dutch)

• Verdonck, Pascal, Marc Van Hulle, Bart De Moor, Erik Mannens, Rudy Mattheus, Geert Molenberghs, Femke Ongenae, Marc Peters, Bart Preneel, and Frank Robben. 2018. “Standpunten: Datawetenschappen En Gezondheidszorg.” http://hdl.handle.net/1854/LU-8553009 (Dutch)

• Kraks@De Krook: Angst. https://www.facebook.com/watch/?v=441924296472619 (Dutch)

• De Morgen, Er komt een app die voorspelt of een baby te vroeg geboren zal-worden. https://www.demorgen.be/nieuws/er-komt-een-app-die-voorspelt-of-een-baby-te-vroeg-geboren-zal-worden~b60a58b2/?fbclid=IwAR0ax3DoOjLzCNC6rCNwOWr27gJv7me8ne5ZpUvJTSD9JR6e7tmHhnn_Epk&utm_source=link&utm_medium=app&utm_campaign=shared%20content&utm_content=free (Dutch)