Predicting 30-Day Emergency Readmission Risk
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.
KeywordsReadmission risk Imbalanced datasets SVM Classification
- 1.World Health Organization: Global health and ageing. World Health Organization, Geneva, Switzerland (2011)Google Scholar
- 4.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)Google Scholar
- 6.Ho, T.K.: Random decision forests. In: 1995 Proceedings of the Third International Conference on Document Analysis and Recognition, pp. 278–282. IEEE (1995)Google Scholar
- 10.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
- 15.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)Google Scholar
- 17.New guidelines for geriatric EDs: guidance focused on boosting environment, care processes. ED Manage 26(5), 49–53 (2014)Google Scholar
- 18.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)Google Scholar
- 19.Hall, M.A.: Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato) (1999)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.