Abstract
An accurate and timely prediction of whether an infection is going to be resistant to a particular antibiotic could improve the clinical outcome of the patient as well as reduce the risk of spreading resistant microorganisms.
From a data analysis perspective, four key factors are present in antimicrobial resistance prediction: the high dimensionality of the data available, the imbalance present in the datasets, the concept drift along time and the need for their acceptance and implantation by clinical staff.
To date, no study has looked specifically at combining different strategies to deal with each of these four key factors. We believe interpretable prediction models are required. This study was undertaken to evaluate the impact of baseline interpretable predicting approaches using a dataset of real hospital data. In particular, we study the capacity of logistic regression, conditional trees and C5.0 rule-based models to improve the prediction when they are combined with oversampling, filtering and sliding windows.
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
Notes
- 1.
R version 3.4.0 from https://cran.r-project.org/.
References
Guidelines for ATC classification and DDD assignment 2018. Technical report, WHO Collaborating Centre for Drug Statistics Methodology, Oslo, Norway (2017). https://www.whocc.no/filearchive/publications/guidelines.pdf. Accessed 28 Aug 2018
Adams, S.T., Leveson, S.H.: Clinical prediction rules. Br. Med. J. 344, d8312 (2012). https://doi.org/10.1136/bmj.d8312
Babyak, M.A.: What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom. Med. 66(3), 411–421 (2004). https://doi.org/10.1097/01.psy.0000127692.23278.a9
Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49(2), 1–50 (2016). https://doi.org/10.1145/2907070
Brzezinski, D., Stefanowski, J.: Ensemble classifiers for imbalanced and evolving data streams. In: Last, M., Bunke, H., Kandel, A. (eds.) Data Mining in Time Series and Streaming Databases, Machine Perception and Artificial Intelligence, vol. 83, pp. 44–68. World Scientific (2018). https://doi.org/10.1142/9789813228047_0003
Cánovas-Segura, B., Campos, M., Morales, A., Juarez, J.M., Palacios, F.: Development of a clinical decision support system for antibiotic management in a hospital environment. Progress Artif. Intell. 5(3), 181–197 (2016). https://doi.org/10.1007/s13748-016-0089-x
Canovas-Segura, B., et al.: Improving interpretable prediction models for antimicrobial resistance. In: 2019 IEEE International Symposium on Computer Medical Systems (CBMS) (2019)
Canovas-Segura, B., et al.: A process-oriented approach for supporting clinical decisions for infection management. In: 2017 IEEE International Conference on Healthcare Informatics (ICHI), pp. 91–100. IEEE (2017). https://doi.org/10.1109/ICHI.2017.73
Cetinkaya, Y., Falk, P., Mayhall, C.G.: Vancomycin-resistant enterococci. Clin. Microbiol. Rev. 13(4), 686–707 (2000). https://doi.org/10.1128/CMR.13.4.686-707.2000
Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1) (2010). https://doi.org/10.18637/jss.v033.i01
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1–37 (2014). https://doi.org/10.1145/2523813
Hastie, T., Tibshirani, R.: Generalized additive models. In: Encyclopedia of Statistical Sciences. Wiley, Hoboken (2006). https://doi.org/10.1002/0471667196.ess0297.pub2
Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15(3), 651–674 (2006). https://doi.org/10.1198/106186006X133933
Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26 (2008). https://doi.org/10.18637/jss.v028.i05
Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6849-3
Leekha, S., Terrell, C.L., Edson, R.S.: General principles of antimicrobial therapy. Mayo Clin. Proc. 86(2), 156–167 (2011). https://doi.org/10.4065/mcp.2010.0639
Lipton, Z.C.: The mythos of model interpretability. CoRR abs/1606.03490 (2016). http://arxiv.org/abs/1606.03490
Mayor, S.: First who antimicrobial surveillance data reveal high levels of resistance globally. Br. Med. J. 462 (2018). https://doi.org/10.1136/bmj.k462
Novoselova, N., Wang, J., Pessler, F., Klawonn, F.: Biocomb: Feature Selection and Classification with the Embedded Validation Procedures for Biomedical Data Analysis. https://cran.r-project.org/web/packages/Biocomb/index.html. Accessed 28 Aug 2018
Palacios, F., et al.: A clinical decision support system for an Antimicrobial Stewardship Program. In: HEALTHINF 2016–9th International Conference on Health Informatics, Proceedings, pp. 496–501. SciTePress, Rome (2016). https://doi.org/10.5220/0005824904960501
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996). https://doi.org/10.1017/CBO9780511812651
Steyerberg, E.: Clinical Prediction Models. Statistics for Biology and Health. Springer, New York (2009)
Tibshirani, R.: Regression selection and shrinkage via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996). https://www.jstor.org/stable/2346178
Tsymbal, A.: The problem of concept drift: definitions and related work. Technical report, Department of Computer Science, Trinity College, Dublin (2004)
Van Tyne, D., Gilmore, M.S.: Friend turned foe: evolution of enterococcal virulence and antibiotic resistance. Ann. Rev. Microbiol. 68(1), 337–356 (2014). https://doi.org/10.1146/annurev-micro-091213-113003
Weiss, G.M., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003). https://doi.org/10.1613/jair.1199
Widmer, G., Miroslav, K.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23, 69–101 (1996). https://doi.org/10.1007/BF00116900
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: International Conference on Machine Learning (ICML), pp. 1–8 (2003)
Acknowledgment
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish Agency for Research (AEI) and by the European Fund for Regional Development (FEDER).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cánovas-Segura, B. et al. (2019). Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-37446-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37445-7
Online ISBN: 978-3-030-37446-4
eBook Packages: Computer ScienceComputer Science (R0)