1. Mellouli, T., Stoeck, T.: Synergies between predictive mining and prescriptive planning of complex patient pathways considering process discrepancies for effective hospital wide decision support. In:Masmoudi, M., Jarboui, B., Siarry, P. (eds.) Artificial intelligence and Data mining in healthcare, SPRINGER (2020)
2. Helbig [Schwarz], K., Mellouli, T., Stoeck, T., Gragert, M., Jahn, P.: Simulation stationsübergreifender Patientenflüsse zur Evaluation flexibler Bettenbelegungsszenarien aufgrund der Jahresdatenanalyse eines Universitätsklinikums. In: MKWI 2014 – Multikonferenz der Wirtschaftsinformatik: 26. – 28. Februar 2014 in Paderborn: Tagungsband, 749–762. University of Paderborn (2014)
3. Helbig [Schwarz], K., Stoeck, T., Mellouli, T.: A Generic Simulation-Based DSS for Evaluating Flexible Ward Clusters in Hospital Occupancy Management. In: IEEE (eds.) Proceedings of the 48th Annual Hawaii International Conference on System Sciences, pp. 2923–2932 (2015)
4. Schwarz, K., Römer M., Mellouli T.: A Data-Driven Hierarchical MILP Approach for Scheduling Clinical Pathways: A Real-World Study from a German University Hospital To appear in BUSINES RESEARCH (2016)
5. Helbig [Schwarz], K., Römer, M., Mellouli. T.: A Clinical Pathway Mining Approach to Enable Scheduling of Hospital Relocations and Treatment Services. In Business Process Management, ed. Hamid Reza Motahari-Nezhad, Jan Recker, and Matthias Weidlich, 9253, pp242–250. Cham: Springer International Publishing (2015)