Cite

ADA (2020). Children and adolescents: Standards of medical care in diabetes—2020, Diabetes Care 43(Suppl 1): S163–S182.10.2337/dc20-S01331862756 Search in Google Scholar

Aspland, E., Gartner, D. and Harper, P. (2019). Clinical pathway modelling: A literature review, Health Systems 0(0): 1–23.10.1080/20476965.2019.1652547794601933758656 Search in Google Scholar

Augusto, V., Xie, X., Prodel, M., Jouaneton, B. and Lamarsalle, L. (2016). Evaluation of discovered clinical pathways using process mining and joint agent-based discrete-event simulation, Proceedings of the 2016 Winter Simulation Conference, Arlington, USA, pp. 2135–2146. Search in Google Scholar

Barber, D. (2012). Bayesian Reasoning and Machine Learning, Cambridge University Press, Cambridge.10.1017/CBO9780511804779 Search in Google Scholar

Bennett, C.C. and Hauser, K.K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach, CoRR abs/1301.2158. Search in Google Scholar

Bourgani, E., Stylios, C., Georgopoulos, V. and Manis, G. (2013). A study on fuzzy cognitive map structures for medical decision support systems, in M. Nikravesh et al. (Eds), Forging New Frontiers: Fuzzy Pioneers II, Springer, Berlin/Heidelberg, pp. 151–174.10.2991/eusflat.2013.111 Search in Google Scholar

Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis, Communications in Statistics—Theory and Methods 3(1): 1–27.10.1080/03610927408827101 Search in Google Scholar

Davidson, M. (2015). Insulin therapy: A personal approach, Clinical Diabetes: A publication of the American Diabetes Association 33(3): 123–135.10.2337/diaclin.33.3.123450394126203205 Search in Google Scholar

De Gaetano, A., Hardy, T., Beck, B., Raddad, E., Palumbo, P., Bue-Valleskey, J. and Pørksen, N. (2008). Mathematical models of diabetes progression, American Journal of Physiology:. Endocrinology and Metabolism 295(6): E1462–79. Search in Google Scholar

Deja, R., Froelich, W. and Deja, G. (2015). Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes, Biomedical Engineering Online 14(1): 13.10.1186/s12938-015-0004-x434967925888901 Search in Google Scholar

Deja, R., Froelich, W., Deja, G. and Wakulicz-Deja, A. (2017). Hybrid approach to the generation of medical guidelines for insulin therapy for children, Information Sciences 384(C): 157–173.10.1016/j.ins.2016.07.066 Search in Google Scholar

Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3(3): 32–57.10.1080/01969727308546046 Search in Google Scholar

Elghazel, H., Deslandres, V., Kallel, K. and Dussauchoy, A. (2007). Clinical pathway analysis using graph-based approach and Markov models, ICDIM 2007 Proceedings, Lyon, France, pp. 279–284. Search in Google Scholar

Froelich, W., Deja, R. and Deja, G. (2013). Mining therapeutic patterns from clinical data for juvenile diabetes, Fundamenta Informaticae 127(1): 513–528.10.3233/FI-2013-924 Search in Google Scholar

Funkner, A.A., Yakovlev, A.N. and Kovalchuk, S.V. (2017). Towards evolutionary discovery of typical clinical pathways in electronic health records, Procedia Computer Science 119: 234–244.10.1016/j.procs.2017.11.181 Search in Google Scholar

García, S., Luengo, J. and Herrera, F. (2015). Data Preprocessing in Data Mining, Intelligent Systems Reference Library, Vol. 72, Springer, Cham. Search in Google Scholar

Haq, A., Wilk, S. and Abelló, A. (2019). Fusion of clinical data: A case study to predict the type of treatment of bone fractures, International Journal of Applied Mathematics and Computer Science 29(1): 51–67, DOI: 10.2478/amcs-2019-0004.10.2478/amcs-2019-0004 Search in Google Scholar

Hripcsak, G., Albers, D. and Perotte, A. (2015). Parameterizing time in electronic health record studies, Journal of the American Medical Informatics Association 22(4): 794–804.10.1093/jamia/ocu051616947125725004 Search in Google Scholar

Huang, Z., Lu, X. and Duan, H. (2012). On mining clinical pathway patterns from medical behaviors, Artificial Intelligence in Medicine 56(1): 35–50.10.1016/j.artmed.2012.06.00222809825 Search in Google Scholar

Marini, S., Trifoglio, E., Barbarini, N., Sambo, F., Di Camillo, B., Malovini, A., Manfrini, M., Cobelli, C. and Bellazzi, R. (2015). A dynamic Bayesian network model for long-term simulation of clinical complications in type 1 diabetes, Journal of Biomedical Informatics 57: 369–376.10.1016/j.jbi.2015.08.02126325295 Search in Google Scholar

Mattila, R., Siika, A., Roy, J. and Wahlberg, B. (2016). A Markov decision process model to guide treatment of abdominal aortic aneurysms, 2016 IEEE Conference on Control Applications (CCA), Buenos Aires, Argentina, pp. 436–441. Search in Google Scholar

Ozcan, Y.A., Tánfani, E. and Testi, A. (2011). A simulation-based modeling framework to deal with clinical pathways, Proceedings of the 2011 Winter Simulation Conference (WSC), Phoenix, USA, pp. 1190–1201. Search in Google Scholar

Palumbo, P., Ditlevsen, S., Bertuzzi, A. and Gaetano, A.D. (2013). Mathematical modeling of the glucose–insulin system: A review, Mathematical Biosciences 244(2): 69–81.10.1016/j.mbs.2013.05.00623733079 Search in Google Scholar

Papiez, A., Badie, C. and Polanska, J. (2019). Machine learning techniques combined with dose profiles indicate radiation response biomarkers, International Journal of Applied Mathematics and Computer Science 29(1): 169–178, DOI: 10.2478/amcs-2019-0013.10.2478/amcs-2019-0013 Search in Google Scholar

Schaefer, A., Bailey, M., Shechter, S. and Roberts, M. (2005). Modeling medical treatment using Markov decision processes, in M.L. Brandeau et al. (Eds), Operations Research and Health Care, Springer, Boston, pp. 593–612.10.1007/1-4020-8066-2_23 Search in Google Scholar

Schwarz, K., Römer, M. and Mellouli, T. (2019). A data-driven hierarchical MILP approach for scheduling clinical pathways: A real-world case study from a German university hospital, Business Research 12: 597–636.10.1007/s40685-019-00102-z Search in Google Scholar

Szwed, P. (2013). Application of fuzzy ontological reasoning in an implementation of medical guidelines, 6th International Conference on Human System Interactions, HSI 2013, Gdańsk, Poland, pp. 1–10. Search in Google Scholar

Weijters, A., Aalst, W. and Medeiros, A. (2006). Process Mining with the Heuristics Miner-Algorithm, Eindhoven University of Technology, Eindhoven. Search in Google Scholar

Xie, X.L. and Beni, G. (1991). A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8): 841–847.10.1109/34.85677 Search in Google Scholar

Yadav, P., Steinbach, M., Kumar, V. and Simon, G. (2017). Mining electronic health records: A survey, arXiv: 1702.03222. Search in Google Scholar

Yang, X., Han, R., Guo, Y., Bradley, J., Cox, B., Dickinson, R. and Kitney, R. (2012). Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA, BMC Bioinformatics 13 (Suppl 14): S4.10.1186/1471-2105-13-S14-S4343972323095226 Search in Google Scholar

Zhang, Y. and Padman, R. (2016). Data-driven clinical and cost pathways for chronic care delivery, The American Journal of Managed Care 22(12): 816–820. Search in Google Scholar

Zhang, Y., Padman, R. and Patel, N. (2015). Paving the cowpath: Learning and visualizing clinical pathways from electronic health record data, Journal of Biomedical Informatics 58: 186–197.10.1016/j.jbi.2015.09.00926419864 Search in Google Scholar

eISSN:
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Language:
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Mathematics, Applied Mathematics