Investigating clinical care pathways correlated with outcomes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8094 LNCS, Issue , 2013, Pages 323-338

  Lakshmanan, Geetika T ^a   Rozsnyai, Szabolcs ^a   Wang, Fei ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Care pathway; clustering; frequent pattern mining; process mining

Indexed keywords

CARE PATHWAY; CLUSTERING; COLLABORATIVE SOFTWARES; CONGESTIVE HEART FAILURES; ELECTRONIC MEDICAL RECORD; FREQUENT PATTERN MINING; HEALTH CARE PROVIDERS; PROCESS MINING;

ENTERPRISE RESOURCE MANAGEMENT; GROUPWARE; MEDICAL COMPUTING; SOFTWARE AS A SERVICE (SAAS); TELEMEDICINE; TOOLS;

DATA MINING;

EID: 84881234799     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40176-3_27     Document Type: Conference Paper

References (36)

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, San Francisco, CA, USA (1994)
2. Aiolli, F., Burattin, A., Sperduti, A.: A business process metric based on the alpha algorithm relations. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 141-146. Springer, Heidelberg (2012)
3. Weijters, A.J.M.M., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristics miner-algorithm. BETA Working Paper (2006)
4. C.P., et al.: Searching electronic health records for temporal patterns in patient histories: A case study with microsoft amalga. In: AMIA Annual Symposium, pp. 601-605 (2008)
5. Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: KDD, pp. 429-435. ACM Press (2002)
6. Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, pp. 401-412 (2009)
7. Jagadeesh Chandra Bose, R.P., van der Aalst, W.: Trace alignment in process mining: Opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227-242. Springer, Heidelberg (2010)
8. Caron, F., Vanthienen, J., De Weerdt, J., Baesens, B.: Advanced care-flow mining and analysis. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011 Workshops, Part I. LNBIP, vol. 99, pp. 167-168. Springer, Heidelberg (2012)
9. Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: ICDE, pp. 169-178 (2008)
10. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Computer (6), 226-231 (1996)
11. Fails, J.A., Karlson, A.K., Shahamat, L., Shneiderman, B.: A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In: IEEE VAST, pp. 167-174 (2006)
12. Goodman, S.N.: Toward evidence-based medical statistics. 1: The p value fallacy. Annals of Internal Medicine 130, 995-1004 (1999)
13. Greco, G., Guzzo, A., Pontieri, L., Saccá, D.: Mining expressive process models by clustering workflow traces. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 52-62. Springer, Heidelberg (2004) (Pubitemid 38824883)
14. Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35-50 (2012)
15. Huang, Z., Lu, X., Duan, H.: Using recommendation to support adaptive clinical pathways. Journal of Medical Systems 36(3), 1849-1860 (2012)
16. Ireson, C.L.: Critical pathways: Effectiveness in achieving patient outcomes. Nursing Administration 27(6), 16-23 (1997)
17. Jung, J.-Y., Bae, J.: Workflow clustering method based on process similarity. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, pp. 379-389. Springer, Heidelberg (2006) (Pubitemid 44045339)
18. Kastner, M., Wagdy Saleh, M., Wagner, S., Affenzeller, M., Jacak, W.: Heuristic methods for searching and clustering hierarchical workflows. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 737-744. Springer, Heidelberg (2009)
19. Lakshmanan, G., Khalaf, R.: Leveraging process mining techniques to analyze semi-structured processes. IT Professional PP (99), 1-1 (2012)
20. Lang, M., Bürkle, T., Laumann, S., Prokosch, H.U.: Process mining for clinical workflows: Challenges and current limitations. In: MIE, pp. 229-234 (2008)
21. de Leoni, M., Adams, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Visual support for work assignment in process-aware information systems: Framework formalisation and implementation. Decision Support Systems 54(1), 345-361 (2012)
22. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707-710 (1966)
23. Ren Lin, F., Chao Chou, S.: Mining time dependency patterns in clinical pathways. International Journal of Medical Informatics, 11-25 (2001)
24. Lo, D., Cheng, H.: Lucia: Mining closed discriminative dyadic sequential patterns. In: International Conference on Extending Database Technology, pp. 21-32 (2011)
25. Mans, R.S., Schonenberg, H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare - a case study in a dutch hospital. In: BIOSTEC (Selected Papers), pp. 425-438 (2008)
26. Mans, R., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: Data challenges when answering frequently posed questions. In: ProHealth/KR4HC, pp. 140-153 (2012)
27. Moskovitch, R., Shahar, Y.: Medical temporal-knowledge discovery via temporal abstraction. In: AMIA Annual Symposium, pp. 452-456 (2009)
28. Norén, G.N., Bate, A., Hopstadius, J., Star, K., Edwards, I.R.: Temporal pattern discovery for trends and transient effects: its application to patient records. In: SIGKDD, pp. 963-971. ACM (2008)
29. Perimal-Lewis, L.: Gaining insight from patient journey data using a process-oriented analysis approach. In: HIKM 2012, vol. 129, pp. 59-66 (2012)
30. Poelmans, J., Dedene, G., Verheyden, G., Van der Mussele, H., Viaene, S., Peters, E.: Combining business process and data discovery techniques for analyzing and improving integrated care pathways. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 505-517. Springer, Heidelberg (2010)
31. Qiao, M., Akkiraju, R., Rembert, A.J.: Towards efficient business process clustering and retrieval: Combining language modeling and structure matching. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 199-214. Springer, Heidelberg (2011)
32. Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: A methodology based on process mining. Inf. Syst. 37(2), 99-116 (2012)
33. Rozsnyai, S., Lakshmanan, G.T., Muthusamy, V., Khalaf, R., Duftler, M.J.: Business process insight: An approach and platform for the discovery and analysis of end-to-end business processes. In: SRII Global Conference, pp. 80-89 (2012)
34. Silva, V., Fernando Chirigati, K.M.A.O., de Oliveira, D., Braganholo, V., Murta, L., Mattoso, M.: Similarity-based workflow clustering. Journal of Computational Interdisciplinary Sciences 2(1), 23-35 (2011)
35. Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109-120. Springer, Heidelberg (2009)
36. Weerdt, J.D., Caron, F., Vanthienen, J., Baesens, B.: Getting a grasp on clinical pathway data: An approach based on process mining. In: PAKDD Workshops, pp. 22-35 (2012)