AIME 2015: Knowledge Representation for Health Care pp 37-50 | Cite as
META-GLARE: A Meta-Engine for Executing Computer Interpretable Guidelines
Abstract
Clinical practice guidelines (CPGs) play an important role in medical practice, and computerized support to CPGs is now one of the most central areas of research in Artificial Intelligence in medicine. In recent years, many groups have developed different computer-assisted management systems of Computer Interpretable Guidelines (CIGs). We propose a generalization: META-GLARE is a “meta”-system (or, in other words, a shell) to define new CIG systems. It takes as input a representation formalism for CIGs, and automatically provides acquisition, consultation and execution engines for it. Our meta-approach has several advantages, such as generality and, above all, flexibility and extendibility. While the meta-engine for acquisition has been already described, in this paper we focus on the execution (meta-)engine.
Keywords
Computer interpretable guideline (CIG) Metamodeling for healthcare systems Meta CIG system System architecture CIG executionNotes
Acknowledgements
The research described in this paper has been partially supported by Compagnia San Paolo, within the GINSENG project.
References
- 1.Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R.A., Hall, R., Johnson, P.D., Jones, N., Kumar, A., Miksch, S., Quaglini, S., Seyfang, A., Shortliffe, E.H., Stefanelli, M.: Comparing computer-interpretable guideline models: a case-study approach. JAMIA 10(1), 52–68 (2003)Google Scholar
- 2.Bottrighi, A., Chesani, F., Mello, P., Montali, M., Montani, S., Storari, S., Terenziani, P.: Analysis of the GLARE and GPROVE approaches to clinical guidelines. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds.) KR4HC 2009. LNCS, vol. 5943, pp. 76–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 3.ten Teije, A., Miksch, S., Lucas, P. (eds.): Computer-Based Medical Guidelines and Protocols: a Primer and Current Trends. IOS Press, Amsterdam (2008)Google Scholar
- 4.Lucas, P., Hommerson, A. (eds.): Foundations of Biomedical Knowledge Representation. Springer, Heidelberg (2015)Google Scholar
- 5.Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46(4), 744–763 (2013)CrossRefGoogle Scholar
- 6.Terenziani, P., Molino, G., Torchio, M.: A modular approach for representing and executing clinical guidelines. Artif. Intell. Med. 23(3), 249–276 (2001)CrossRefGoogle Scholar
- 7.Terenziani, P., Montani, S., Bottrighi, A., Molino, G., Torchio, M.: Applying artificial intelligence to clinical guidelines: the GLARE approach. In: [3], 273–282 (2008)Google Scholar
- 8.Terenziani, P., Bottrighi, A., Lovotti, I., Rubrichi, S.: META-GLARE: a meta-system for defining your own CIG system: architecture and acquisition. In: Miksch, S., Riano, D., ten Teije, A. (eds.) KR4HC 2014. LNCS, vol. 8903, pp. 95–110. Springer, Heidelberg (2014)Google Scholar
- 9.Isern, D., Moreno, A.: Computer-based execution of clinical guidelines: A review. Int. J. Med. Inform. 77, 787–808 (2008)CrossRefGoogle Scholar
- 10.Russel, S., Norving, P.: Artificial Intelligence: a Modern Approach. Prentice Hall, New Jersey (2009)Google Scholar
- 11.Anselma, L., Bottrighi, A., Molino, G., Montani, S., Terenziani, P., Torchio, M.: Supporting knowledge-based decision making in the medical context: the GLARE approach. IJKBO 1(1), 42–60 (2011)Google Scholar
- 12.Sutton, D.R., Fox, J.: The syntax and semantics of the PROforma guideline modeling language. J. Am. Med. Inform. Assoc. 10, 433–443 (2003)CrossRefGoogle Scholar
- 13.InferMed, Arezzo Technical White Paper, Technical report InferMed, Ltd. http://www.infermed.com/ Accessed 18 May 2015
- 14.Isern, D., Sánchez, D., Moreno, A.: HeCaSe2: A Multi-agent Ontology-Driven Guideline Enactment Engine. In: Burkhard, H.-D., Lindemann, G., Verbrugge, R., Varga, L.Z. (eds.) CEEMAS 2007. LNCS (LNAI), vol. 4696, pp. 322–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 15.Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, M.A., McClure, R., McClay, J., Parker, C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, M.A., Abarbanel, R.M.: The SAGE guideline model: achievements and overview. JAMIA 14(5), 589–598 (2007)Google Scholar
- 16.Wang, D., Peleg, M., Tu, S.W., Boxwala, A.A., Ogunyemi, O., Zeng, Q., Greenes, R.A., Patel, V.L., Shortliffe, E.H.: Design and implementation of the GLIF3 guideline execution engine. J. Biomed. Inform. 37, 305–318 (2004)CrossRefGoogle Scholar
- 17.Young, O., Shahar, Y., Liel, Y., Lunenfeld, E., Bar, G., Shalom, E., Martins, S.B., Vaszar, L.T., Marom, T., Goldstein, M.K.: Runtime application of Hybrid-Asbru clinical guidelines. J. Biomed. Inform. 40, 507–526 (2007)CrossRefGoogle Scholar
- 18.Johnson, S.C.: Yacc: Yet Another Compiler-Compiler, vol. 32. Bell Laboratories, Murray Hill, NJ (1975)Google Scholar
- 19.Leonardi, G., Bottrighi, A., Galliani, G., Terenziani, P., Messina, A., Della Corte, F.: Exceptions handling within GLARE clinical guideline framework. AMIA Annu. Symp. Proc. 2012, 512–521 (2012)Google Scholar
- 20.Piovesan, L., Molino, G., Terenziani, P.: Supporting Physicians in the Detection of the Interactions between Treatments of Co-Morbid Patients, In: Tavana, M., Ghapanchi, A.H., Talaei-Khoei A. (Eds.) Healthcare Informatics and Analytics: Emerging Issues and Trends, Chapter: 9, IGI Global (2014)Google Scholar