Abstract
Inappropriate antimicrobial prescribing is a major clinical problem and health concern. Several hospitals rely on automated surveillance to achieve hospital-wide antimicrobial optimization. The main challenge in implementing these systems lies in acquiring and updating their knowledge. In this paper, we discuss a surveillance system which can acquire new rules and improve its knowledge base. Our system uses an algorithm based on instance-based learning and rule induction to discover rules for inappropriate prescriptions. The algorithm uses temporal abstraction to extract a meaningful time interval representation from raw clinical data, and applies nearest neighbor classification with a distance function on both temporal and non-temporal parameters. The algorithm is able to discover new rules for early switch from intravenous to oral antimicrobial therapy from real clinical data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dellit, T.H., Owens, R.C., McGowan, J.E., Gerding, D.N., Weinstein, R.A., Burke, J.P., Huskins, W.C., Paterson, D.L., Fishman, N.O., Carpenter, C.F., Brennan, P.J., Billeter, M., Hooton, T.M.: Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin. Infect. Dis. 44(2), 159–177 (2007)
Valiquette, L., Cossette, B., Garant, M.P., Diab, H., Pepin, J.: Impact of a reduction in the use of high-risk antibiotics on the course of an epidemic of clostridium difficile-associated disease caused by the hypervirulent nap1/027 strain. Clin. Infect. Dis. 45(suppl. 2), 112–121 (2007)
Shahar, Y.: A framework for knowledge-based temporal abstraction. Artif. Intell. 90(1-2), 79–133 (1997)
Bellazzi, R., Larizza, C., Magni, P., Bellazzi, R.: Temporal data mining for the quality assessment of a hemodialysis service. Artif. Intell. Med. 34(1), 25–39 (2005)
Concaro, S., Sacchi, L., Cerra, C., Fratino, P., Bellazzi, R.: Mining healthcare data with temporal association rules: Improvements and assessment for a practical use. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS, vol. 5651, pp. 16–25. Springer, Heidelberg (2009)
Zaki, M., Lesh, N., Ogihara, M.: Planmine: Predicting plan failures using sequence mining. Artif. Intell. Rev. 14(6), 421–446 (2000)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th Int. Conf. on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Hartge, F., Wetter, T., Haefeli, W.E.: A similarity measure for case based reasoning modeling with temporal abstraction based on cross-correlation. Comput. Methods Programs Biomed. 81(1), 41–48 (2006)
Montani, S., Portinale, L., Leonardi, G.: Case-based retrieval to support the treatment of end stage renal failure patients. Artif. Intell. Med. 37(1), 31–42 (2006)
Domingos, P.: Unifying instance-based and rule-based induction. Machine Learning 24(2), 141–168 (1996)
Chawla, N., Japkowicz, N., Kotcz, A.: Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6(1), 1–6 (2004)
Smyth, P., Goodman, R.M.: Rule induction using information theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. AAAI/MIT Press (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beaudoin, M., Kabanza, F., Nault, V., Valiquette, L. (2013). Learning to Identify Inappropriate Antimicrobial Prescriptions. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-38326-7_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38325-0
Online ISBN: 978-3-642-38326-7
eBook Packages: Computer ScienceComputer Science (R0)