Abstract
Objective: Predicting Emergency Department (ED) readmissions is of great importance since it helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. It is becoming standard procedure to evaluate the risk of ED readmission within 30 days after discharge. Methods. Our dataset is stratified into four groups according to the Kaiser Permanente Risk Stratification Model. We deal with imbalanced data using different approaches for resampling. Feature selection is also addressed by a wrapper method which evaluates feature set importance by the performance of various classifiers trained on them. Results. We trained a model for each scenario and subpopulation, namely case management (CM), heart failure (HF), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Using the full dataset we found that the best sensitivity is achieved by SVM using over-sampling methods (40.62 % sensitivity, 78.71 % specificity and 71.94 accuracy). Conclusions. Imbalance correction techniques allow to achieve better sensitivity performance, however the dataset has not enough positive cases, hindering the achievement of better prediction ability. The arbitrary definition of a threshold-based discretization for measurements which are inherently is an important drawback for the exploitation of the data, therefore a regression approach is considered as future work.
This is a preview of subscription content, log in via an institution to check access.
References
World Health Organization: Global health and ageing. World Health Organization, Geneva, Switzerland (2011)
Besga, A., Ayerdi, B., Alcalde, G., et al.: Risk factors for emergency department short time readmission in stratified population. BioMed Res. Int. 2015, 7 pages (2015). Article ID 685067, doi:10.1155/2015/685067
Van Walraven, C., et al.: Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Can. Med. Assoc. J. 182(6), 551–557 (2010)
Van Walraven, C., Wong, J., Forster, A.: LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med. 6(3), 80–89 (2012)
Yu, S., Farooq, F., van Esbroeck, A., Fung, G., Anand, V., Krishnapuram, B.: Predicting readmission risk with institution-specific prediction models. Artif. Intell. Med. 65(2), 89–96 (2015)
Ho, T.K.: Random decision forests. In: 1995 Proceedings of the Third International Conference on Document Analysis and Recognition, pp. 278–282. IEEE (1995)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., Kripalani, S.: Risk prediction models for hospital readmission: a systematic review. JAMA 306(15), 1688–1698 (2011)
Health Quality Ontario - Early Identification of People At-Risk of Hospitalization. ISBN 978-1-4606-2908-6 (PDF) Queen’s Printer for Ontario (2013). Accessed 09 Mar 2016. Enlace: https://secure.cihi.ca/free_products/HARP_reportv_En.pdf
Feachem, R.G., Dixon, J., Berwick, D.M., Enthoven, A.C., Sekhri, N.K., White, K.L.: Getting more for their dollar: a comparison of the NHS with California’s Kaiser Permanente. BMJ 324(7330), 135–143 (2002)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)
Carpenter, C.R., Heard, K., Wilber, S., Ginde, A.A., Stiffler, K., Gerson, L.W., et al.: Research priorities for high-quality geriatric emergency care: medication management, screening, and prevention and functional assessment. Acad. Emerg. Med. 18(6), 644–654 (2011)
Lopez-Aguila, S., Contel, J.C., Farre, J., Campuzano, J.L., Rajmil, L.: Predictive model for emergency hospital admission and 6-month readmission. Am. J. Manage. Care 17(9), e348–e357 (2011)
Han, J.H., Zimmerman, E.E., Cutler, N., Schnelle, J., Morandi, A., Dittus, R.S., et al.: Delirium in older emergency department patients: recognition, risk factors, and psychomotor subtypes. Acad. Emerg. Med. 16(3), 193–200 (2009)
New guidelines for geriatric EDs: guidance focused on boosting environment, care processes. ED Manage 26(5), 49–53 (2014)
Phuong, T.M., Lin, Z., Altman, R.B.: Choosing SNPs using feature selection. In: 2005 IEEE Computational Systems Bioinformatics Conference (CSB 2005), pp. 301–309. IEEE (2005)
Hall, M.A.: Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato) (1999)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Artetxe, A., Beristain, A., Graña, M., Besga, A. (2017). Predicting 30-Day Emergency Readmission Risk. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-47364-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47363-5
Online ISBN: 978-3-319-47364-2
eBook Packages: EngineeringEngineering (R0)