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. 

LLMs & Knowledge Graphs

• Vandemoortele, Nathan, Bram Steenwinckel, Sofie Van Hoecke, and Femke Ongenae. 2024. “Scalable Table-to-Knowledge Graph Matching from Metadata Using LLMs.” In SemTab 2024 : Semantic Web Challenge on Tabular Data to Knowledge Graph Matching 2024 , Co-Located with the 23rd International Semantic Web Conference (ISWC 2024), 3889:54–68. CEUR. https://ceur-ws.org/Vol-3889/paper4.pdf 

• Salatino, Angelo, Mehwish Alam, Femke Ongenae, Sahar Vahdati, Anna-Lisa Gentile, Tassilo Pellegrini, and Shufan Jiang, eds. 2024. “Knowledge Graphs in the Age of Language Models and Neuro-Symbolic AI.” IOS Press. https://doi.org/10.3233/ssw60 

Chatbots (LLMs) for Behavioral Change

• Kashefi, Arian, Kyana Bosschaerts, Femke Ongenae, Lieven De Marez, Stephen Lee Murphy, Peter Conradie, and Mariek Vanden Abeele. 2025. “Building Digital Therapeutic Alliance with Smoking Cessation Chatbots : An Experimental Examination of Communication Style and User Personality.” INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 1–22. https://doi.org/10.1080/10447318.2025.2598453

• Kashefi, Arian, Kyana Bosschaerts, Lieven De Marez, Femke Ongenae, and Peter Conradie. 2025. “Lost in Translation? Assessing User Perception of Prompt-Engineered Chatbot Tone-of-Voice for Smoking Cessation.” https://doi.org/10.2139/ssrn.5630090 

• Bosschaerts, Kyana, Arian Kashefi, Lieven De Marez, Peter Conradie, Sofie Van Hoecke, and Femke Ongenae. 2025. “Designing a Just-in-Time Adaptive Intervention with Trigger Detection and a Generative Chatbot : Smoking Cessation Use Case.” DIGITAL HEALTH 11. https://doi.org/10.1177/20552076251381747 

• Bosschaerts, Kyana, Jeroen Stragier, Bram Steenwinckel, Lieven De Marez, Sofie Van Hoecke, and Femke Ongenae. 2023. “Design of a Social Chatbot with Gamification for User Profiling and Smoking Trigger Detection.” In PERVASIVE COMPUTING TECHNOLOGIES FOR HEALTHCARE, PERVASIVEHEALTH 2022, 488:636–50. Springer. https://doi.org/10.1007/978-3-031-34586-9_42 

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

• Weyns, Michael, Thibault Blyau, Bram Steenwinckel, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2025. “Explainable Knowledge Graph Embeddings for Industrial Process Monitoring & Control.” INFORMATION FUSION 123. https://doi.org/10.1016/j.inffus.2025.103242 

• Weyns, Michael, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Pieter Bonte, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2025. “Enabling Cross-Context Learning with Knowledge Graphs for Anomaly Detection in Communications Networks.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 33 (4). https://doi.org/10.1007/s10922-025-09953-w 

• Steenwinckel, Bram, Filip De Turck, and Femke Ongenae. 2024. “INK : Knowledge Graph Representation for Efficient and Performant Rule Mining.” SEMANTIC WEB 15 (4): 1367–88. https://doi.org/10.3233/sw-233495. 

• Bonte, Pieter, Sander Vanden Hautte, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2024. “Benchmarking Whole Knowledge Graph Embedding Techniques.” INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 34 (1): 163–84. https://doi.org/10.1142/S0218194023500481 

• Steenwinckel, Bram, Gilles Vandewiele, Terencio Agozzino, and Femke Ongenae. 2023. “PyRDF2Vec : A Python Implementation and Extension of RDF2Vec.” In SEMANTIC WEB, ESWC 2023, edited by C Pesquita, E Jimenez-Ruiz, J McCusker, D Faria, M Dragoni, A Dimou, R Troncy, and S Hertling, 13870:471–83. Springer Cham. https://doi.org/10.1007/978-3-031-33455-9_28 

• Steenwinckel, Bram, Mathias De Brouwer, Marija Stojchevska, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2023. “TALK : Tracking Activities by Linking Knowledge.” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 122. https://doi.org/10.1016/j.engappai.2023.106076 

• 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

• Verhaeghe, Jarne, Jeroen Van Der Donckt, Femke Ongenae, and Sofie Van Hoecke. 2023. “Powershap : A Power-Full Shapley Feature Selection Method.” In MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I, edited by Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, and Grigorios Tsoumakas, 13713:71–87. Springer. https://doi.org/10.1007/978-3-031-26387-3_5 

• 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

Predictive maintenance (anomaly detection, root cause analysis)

• Steenwinckel, Bram, Colin Soete, Pieter Moens, Joris Mussche, Sofie Van Hoecke, and Femke Ongenae. 2025. “Quality in Color : Using Knowledge Graphs for Enhanced Quality Control in an Automotive Paintshop.” In SEMANTIC WEB-ISWC 2024, PT III, edited by G. Demartini, K. Hose, M. Acosta, M. Palmonari, G. Cheng, H. Skaf-Molli, N. Ferranti, D. Hernandez, and A. Hogan, 15233:236–52. Springer. https://doi.org/10.1007/978-3-031-77847-6_13 

• Weyns, Michael, Ganjour Mazaev, Guido Vaes, Filip Vancoillie, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2023. “Leak Localization in Water Distribution Networks Using GIS-Enhanced Autoencoders.” URBAN WATER JOURNAL 20 (7): 859–81. https://doi.org/10.1080/1573062X.2023.2216191 

• 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). http://hdl.handle.net/1854/LU-8583136  

Scalabe & Distributed Semantic reasoning & Solid

• Vandenbrande, Maarten, Ruben Taelman, Pieter Bonte, and Femke Ongenae. 2025. “Incremunica : Web-Based Incremental View Maintenance for SPARQL.” In The Semantic Web : 22nd European Semantic Web Conference, ESWC 2025, Proceedings, Part II, edited by Edward Curry, Maribel Acosta, Maria Poveda-Villalón, Marieke van Erp, Adegboyega Ojo, Katja Hose, Cogan Shimizu, and Pasquale Lisena, 15719:297–315. Cham: Springer. https://doi.org/10.1007/978-3-031-94578-6_17 

• Hochstenbach, Patrick, Mathijs van Noort, Dörthe Arndt, Rebekka Martens, Jos De Roo, Ruben Verborgh, Pieter Bonte, and Femke Ongenae. 2024. “RDF Surfaces : Enabling Classical Negation on the Semantic Web.” SEMANTIC WEB. doi: https://arxiv.org/abs/2406.10659 

• De Brouwer, Mathias, Filip De Turck, and Femke Ongenae. 2024. “Enabling Efficient Semantic Stream Processing across the IoT Network through Adaptive Distribution with DIVIDE.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 32 (2). https://doi.org/10.1007/s10922-023-09797-2 

• 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 

Stream reasoning

• Bonte, Pieter, Femke Ongenae, and Riccardo Tommasini. 2024. “A Holistic View over Ontologies for Streaming Linked Data.” SEMANTIC WEB 15 (5): 2005–33. https://doi.org/10.3233/SW-243570 

• Windels, Tom, Wout Slabbinck, Pieter Bonte, Stijn Verstichel, Pieter Colpaert, Sofie Van Hoecke, and Femke Ongenae. 2023. “LDESTS : Enabling Efficient Storage and Querying of Large Volumes of Time Series Data on Solid Pods.” In Posters, Demos, and Industry Tracks at ISWC 2023 . Vol. 3632. CEUR.  https://ceur-ws.org/Vol-3632/ISWC2023_paper_425.pdf 

• 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 

Machine learning for healthcare

• Stojchevska, Marija, Jonas Van Der Donckt, Nicolas Vandenbussche, Mathias De Brouwer, Koen Paemeleire, Femke Ongenae, and Sofie Van Hoecke. 2025. “Uncovering the Potential of Smartphones for Behavior Monitoring during Migraine Follow-Up.” BMC MEDICAL INFORMATICS AND DECISION MAKING 25 (1). https://doi.org/10.1186/s12911-025-02916-w 

• Stojchevska, Marija, Mathias De Brouwer, Martijn Courteaux, Bram Steenwinckel, Sofie Van Hoecke, and Femke Ongenae. 2024. “Unlocking the Potential of Smartphone and Ambient Sensors for ADL Detection.” SCIENTIFIC REPORTS 14 (1). https://doi.org/10.1038/s41598-024-56123-0 

• Stojchevska, Marija, Mathias De Brouwer, Martijn Courteaux, Femke Ongenae, and Sofie Van Hoecke. 2023. “From Lab to Real World : Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization.” SENSORS 23 (10). https://doi.org/10.3390/s23104606 

• Stojchevska, Marija, Bram Steenwinckel, Jonas Van Der Donckt, Mathias De Brouwer, Annelies Goris, Filip De Turck, Sofie Van Hoecke, and Femke Ongenae. 2022. “Assessing the Added Value of Context during Stress Detection from Wearable Data.” BMC MEDICAL INFORMATICS AND DECISION MAKING 22 (1). https://doi.org/10.1186/s12911-022-02010-5 

• 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

• De Brouwer, Mathias, Pieter Bonte, Doerthe Arndt, Miel Vander Sande, Anastasia Dimou, Ruben Verborgh, Filip De Turck, and Femke Ongenae. 2024. “Optimized Continuous Homecare Provisioning through Distributed Data-Driven Semantic Services and Cross-Organizational Workflows.” JOURNAL OF BIOMEDICAL SEMANTICS 15 (1). https://doi.org/10.1186/s13326-024-00303-4 

• 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 

(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)