Supporting healthcare management decisions via robust clustering of event logs

Knowledge-Based Systems

Volumn 84, Issue , 2015, Pages 203-213

  Delias, Pavlos ^a   Doumpos, Michael ^b   Grigoroudis, Evangelos ^b   Manolitzas, Panagiotis ^b   Matsatsinis, Nikolaos ^b  

^a TECHNOLOGICAL EDUCATIONAL INSTITUTE OF KAVALA   (Greece)

^b TECHNICAL UNIVERSITY OF CRETE   (Greece)

Author keywords

Clustering; Healthcare management; Knowledge discovery; Process mining; Robustness

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; HEALTH CARE; ROBUSTNESS (CONTROL SYSTEMS);

CLUSTERING; FLEXIBLE ENVIRONMENTS; HEALTH-CARE MANAGEMENTS; HEALTHCARE INSTITUTIONS; PROCESS MINING; SIMILARITY METRICS; SPECTRAL TECHNIQUES; WORKING ENVIRONMENT;

DECISION MAKING;

EID: 84929507434     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.04.012     Document Type: Article

References (46)

1. R. Bellazzi, F. Ferrazzi, and L. Sacchi Predictive data mining in clinical medicine: a focus on selected methods and applications WIREs Data Min. Knowl. Discov. 1 5 2011 416 430
2. R.P.J.C. Bose, W.M.P. Van der Aalst, Context aware trace clustering: towards improving process mining results, in: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, Sparks, Nevada, 2009, pp. 401-412.
3. R.P.J.C. Bose, and W.M.P. Van der Aalst Trace clustering based on conserved patterns: towards achieving better process models S. Rinderle-Ma, S. Sadiq, F. Leymann, Business Process Management Workshops 2010 Springer Berlin, Heidelberg 170 181
4. J. Cardoso Evaluating the process control-flow complexity measure 2005 IEEE International Conference on Web Services 2005 IEEE Computer Society Press Los Alamitos 803 804
5. J. Cardoso Process control-flow complexity metric: an empirical validation IEEE International Conference on Services Computing, 2006 (SCC 2006) 2006 IEEE Computing Society Press Los Alamitos 167 173
6. H. Chang, and D.-Y. Yeung Robust path-based spectral clustering Pattern Recogn. 41 1 2008 191 203
7. J.H. Chang Mining weighted sequential patterns in a sequence database with a time-interval weight Knowl.-Based Syst. 24 1 2011 1 9
8. P. Delias, P. Manolitzas, E. Grigoroudis, and N. Matsatsinis Applying process mining to the emergency department J. Wang, Encyclopedia of Business Analytics and Optimization 2014 IGI Global Hershey, PA 168 178
9. R. Dijkman, M. Dumas, B. Van Dongen, R. Käärik, and J. Mendling Similarity of business process models: metrics and evaluation Inform. Syst. 36 2 2011 498 516
10. M. Dumas, M. La Rosa, J. Mendling, and H.A. Reijers Fundamentals of Business Process Management 2013 Springer Berlin, Heidelberg
11. G. Greco, A. Guzzo, L. Pontieri, and D. Saccà Mining expressive process models by clustering workflow traces H. Dai, R. Srikant, C. Zhang, Advances in Knowledge Discovery and Data Mining 2004 Springer Berlin, Heidelberg 52 62
12. F. Guil, and R. Marín A tree structure for event-based sequence mining Knowl.-Based Syst. 35 2012 186 200
13. C.W. Günther, Process Mining in Flexible Environments, PhD Dissertation, Technische Universiteit Eindhoven, Eindhoven, 2009.
14. C.W. Günther, and W.M.P. Van der Aalst Fuzzy mining: adaptive process simplification based on multi-perspective metrics G. Alonso, P. Dadam, M. Rosemann, Business Process Management 2007 Springer Berlin, Heidelberg 328 343
15. R. Henriques, and C. Antunes Learning predictive models from integrated healthcare data: extending pattern-based and generative models to capture temporal and cross-attribute dependencies 47th Hawaii International Conference on System Sciences (HICSS 2014) 2014 IEEE Hawaii 2562 2569
16. Z. Huang, W. Dong, H. Duan, and H. Li Similarity measure between patient traces for clinical pathway analysis: problem, method, and applications IEEE J. Biomed. Health Inform. 18 1 2014 4 14
17. Z. Huang, X. Lu, H. Duan, and W. Fan Summarizing clinical pathways from event logs J. Biomed. Inform. 46 1 2013 111 127
18. J.-Y. Jung, J. Bae, and L. Liu Hierarchical clustering of business process models Int. J. Innov. Comput. Inform. Control 12 5 2009 1349 4198
19. U. Kaymak, R. Mans, T. Van de Steeg, and M. Dierks On process mining in health care 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2012 IEEE Seoul 1859 1864
20. R. Keeney, and H. Raiffa Decisions with Multiple Objectives: Preferences and Value Trade-Offs 1976 Wiley New York
21. K.B. Lassen, and W.M.P. Van der Aalst Complexity metrics for workflow nets Inf. Softw. Technol. 51 3 2009 610 626
22. R. Lenz, S. Miksch, M. Peleg, M. Reichert, D. Riaño, and A. ten Teije Process Support and Knowledge Representation in Health Care, Lecture Notes in Artificial Intelligence first ed. 2013 Springer New York
23. D. Luengo, and M. Sepúlveda Applying clustering in process mining to find different versions of a business process that changes over time F. Daniel, K. Barkaoui, S. Dustdar, Business Process Management Workshops 2012 Springer Berlin, Heidelberg 153 158
24. J. Mendling, Testing Density as a Complexity Metric for EPCs, Technical Report No. JM-2006-11-15, Vienna University of Economics and Business Administration, Vienna, 2006.
25. J. Mendling, M. Moser, G. Neumann, H.M.W. Verbeek, B.F. Van Dongen, and W.M.P. Van der Aalst Faulty EPCs in the SAP reference model S. Dustdar, J. Fiadeiro, A. Sheth, Business Process Management, Lecture Notes in Computer Science 2006 Springer Berlin, Heidelberg 451 457
26. P. Meyer, and A.-L. Olteanu Formalizing and solving the problem of clustering in MCDA Eur. J. Oper. Res. 227 3 2013 494 502
27. B. Nadler, S. Lafon, R. Coifman, and I. Kevrekidis Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators Y. Weiss, B. Schölkopf, J. Platt, Advances in Neural Information Processing Systems, vol. 18 2006 MIT Press Cambridge, MA 955 962
28. A.Y. Ng, M.I. Jordan, and Y. Weiss On spectral clustering: analysis and an algorithm T.G. Dietterich, S. Becker, Z. Ghahramani, Advances in Neural Information Processing Systems, vol. 14 2001 MIT Press Cambridge, MA 849 856
29. D. Patnaik, P. Butler, N. Ramakrishnan, L. Parida, B.J. Keller, and D.A. Hanauer Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2011 ACM Press 360 368
30. Á. Rebuge, and D.R. Ferreira Business process analysis in healthcare environments: a methodology based on process mining Inform. Syst. 37 2 2012 99 116
31. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 22 8 2000 888 905
32. Y. Siskos, E. Grigoroudis, and N. Matsatsinis UTA methods J. Figueira, S. Greco, M. Ehrgott, Multiple Criteria Decision Analysis: State of the Art Surveys 2005 Springer New York 297 344
33. M. Song, C.W. Günther, and W.M.P. Van der Aalst Trace clustering in process mining D. Ardagna, M. Mecella, J. Yang, Business Process Management Workshops 2009 Springer Berlin, Heidelberg 109 120
34. S. Suriadi, R. Mans, M. Wynn, A. Partington, and J. Karnon Measuring patient flow variations: a cross-organisational process mining approach C. Ouyang, J.-Y. Jung, Asia Pacific Business Process Management Lecture Notes in Business Information Processing 2014 Springer New York 43 58
35. W.M.P. Van der Aalst Process Mining: Discovery, Conformance, and Enhancement of Business Processes 2011 Springer Berlin, Heidelberg
36. W.M.P. Van der Aalst Process mining: overview and opportunities ACM Trans. Manage. Inform. Syst. 3 2 2012 1 17
37. W.M.P. Van der Aalst, A. Adriansyah, A.K.A. Medeiros, F. Arcieri, and T. Baier Process mining manifesto F. Daniel, K. Barkaoui, S. Dustdar, Business Process Management Workshops 2012 Springer Berlin, Heidelberg 169 194
38. W.M.P. Van der Aalst, T. Weijters, and L. Maruster Workflow mining: discovering process models from event logs IEEE Trans. Knowl. Data Eng. 16 9 2004 1128 1142
39. I. Van der Feesten, J. Cardoso, J. Mendling, H.A. Reijers, and W.M.P. Van der Aalst Quality metrics for business process models L. Fischer, BPM and Workflow Handbook, vol. 144 2007 Future Strategies Inc. Lighthouse Point, FL 179 190
40. J.M.E.M. Van der Werf, B.F. van Dongen, C.A.J. Hurkens, and A. Serebrenik Process discovery using integer linear programming K.M. Van Hee, R. Valk, Applications and Theory of Petri Nets 2008 Springer Berlin, Heidelberg 368 387
41. G.M. Veiga, and D.R. Ferreira Understanding spaghetti models with sequence clustering for ProM S. Rinderle-Ma, S. Sadiq, F. Leymann, Business Process Management Workshops 2010 Springer Berlin, Heidelberg 92 103
42. M. Velikova, J.T. Van Scheltinga, P.J.F. Lucas, and M. Spaanderman Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare Int. J. Approx. Reason. 55 1 2014 59 73
43. D. Verma, M. Meila, A Comparison of Spectral Clustering Algorithms, Technical Report No. 98195-2350, Department of CSE, University of Washington, Seattle WA, 2005.
44. U. Von Luxburg A tutorial on spectral clustering Stat. Comput. 17 4 2007 395 416
45. A.J.M.M. Weijters, and W.M.P. Van der Aalst Rediscovering workflow models from event-based data using little thumb Integr. Comput.-Aided Eng. 10 2 2003 151 162
46. I. Yoo, P. Alafaireet, M. Marinov, K. Pena-Hernandez, R. Gopidi, J.-F. Chang, and L. Hua Data mining in healthcare and biomedicine: a survey of the literature J. Med. Syst. 36 4 2012 2431 2448