Learning the context of a clinical process

Lecture Notes in Business Information Processing

Volumn 43 LNBIP, Issue , 2010, Pages 545-556

  Ghattas, Johny ^a   Peleg, Mor ^a,b   Soffer, Pnina ^a   Denekamp, Yaron ^c  

^a UNIVERSITY OF HAIFA   (Israel)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c CARMEL MEDICAL CENTER   (Israel)

Author keywords

business process learning; Clinical guidelines; context; flexibility; process goals; process model adaptation; soft goals

Indexed keywords

DECISION MAKING; ENTERPRISE RESOURCE MANAGEMENT; FORMAL LOGIC;

BUSINESS PROCESS; CLINICAL GUIDELINE; CLINICAL PROCESS; FORMAL PROCESS MODELS; GROUP PROCESS; MACHINE LEARNING ALGORITHMS; MAKING DECISION; MANAGEMENT PROCESS; MEDICAL CARE; PROCESS EXECUTION; PROCESS GOALS; PROCESS MODEL; URINARY TRACT INFECTIONS;

LEARNING ALGORITHMS;

EID: 77953981480     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-12186-9_53     Document Type: Conference Paper

References (14)

1. Field, M.J., Lohr, K.N.: Guidelines for Clinical Practice: Directions for a New Program. Institute of Medicine, National Academy Press, Washington (1990)
2. Ghattas, J., Soffer, P., Peleg, M.: A Goal-based approach for business process learning. In: Workshop on Business Process Modeling, Development, and Support (BPMDS 2008), in conjunction with CAISE 2008, Montpellier, France (2008)
3. Ploesser, K., Peleg, M., Soffer, P., Rosemann, M., Recker, J.: Learning from Context to Improve Business Processes. In: BPtrends 2009 (1), pp. 1-9 (2009)
4. Ghattas, J., Soffer, P., Peleg, M.: A formal model for Process context learning. In: 5th workshop on Business process intelligence, Ulm, Germany (2009)
5. SPSS corporation. SPSS statistics software, version 16 (2009), http://www.spss.com
6. Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716-723 (1974)
7. Kass, G.V.: An Exploratory Technique for Investigating Large Quantities of Categorical Data. Journal of Applied Statistics 29(2), 119-127 (1980)
8. Geisser, S.: Predictive Inference. Chapman and Hall, New York (1993)
9. Simon, D., Boring, J.R.: Sensitivity, Specificity, and Predictive Value. In: Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd edn. (1990)
10. Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, M.A., McClure, R.J., McClay, J.P.C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, M.A., Abarbanel, R.M.: The SAGE Guideline Model: achievements and overview. J. Am. Med. Inform. Assoc. 14(5), 589-598 (2007)
11. Russler, D.C., Schadow, G., Mead, C., Snyder, T., Quade, L.M., McDonald, C.J.: Influences of the Unified Service Action Model on the HL7 Reference Information Model. In: Proc. AMIA Symp., pp. 930-934 (1999)
12. Mans, R.S., Schonenberg, M.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. BIOSTEC 25, 425-438 (2008)
13. Aamodt, A.E.: EP. Case based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39-59 (1994)
14. Weber, B., Rinderle, S., Wild, W., Reichert, M.: CCBR-Driven Business Process Evolution. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 610-624. Springer, Heidelberg (2005)