Abstract
Short time readmission prediction in Emergency Departments (ED) is a valuable tool to improve both the ED management and the healthcare quality. It helps identifying patients requiring further post-discharge attention as well as reducing healthcare costs. As in many other medical domains, patient readmission data is heavily imbalanced, i.e. the minority class is very infrequent, which is a challenge for the construction of accurate predictors using machine learning tools. We have carried computational experiments on a dataset composed of ED admission records spanning more than 100000 patients in 3 years, with a highly imbalanced distribution. We employed various approaches for dealing with this highly imbalanced dataset in combination with different classification algorithms and compared their predictive power for the estimation of the ED readmission probability within 72 h after discharge. Results show that random undersampling and Bagging (RUSBagging) in combination with Random Forest achieves the best results in terms of Area Under ROC Curve (AUC).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Most common categorical values are only shown.
- 2.
References
Artetxe, A., Beristain, A., Graña, M., Besga, A.: Predicting 30-day emergency readmission risk. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) ICEUTE/SOCO/CISIS -2016. AISC, vol. 527, pp. 3–12. Springer, Cham (2017). doi:10.1007/978-3-319-47364-2_1
Billings, J., Blunt, I., Steventon, A., Georghiou, T., Lewis, G., Bardsley, M.: Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (parr-30). BMJ Open 2(4), e001667 (2012)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
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)
Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS, vol. 2838, pp. 107–119. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39804-2_12
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(4), 463–484 (2012)
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)
Khalilia, M., Chakraborty, S., Popescu, M.: Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inform. Decis. Mak. 11(1), 1 (2011)
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)
Mateo, F., Soria-Olivas, E., Martınez-Sober, M., Téllez-Plaza, M., Gómez-Sanchis, J., Redón, J.: Multi-step strategy for mortality assessment in cardiovascular risk patients with imbalanced data. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2016)
Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427–436 (2008)
Meadem, N., Verbiest, N., Zolfaghar, K., Agarwal, J., Chin, S.C., Roy, S.B.: Exploring preprocessing techniques for prediction of risk of readmission for congestive heart failure patients. In: International Conference on Knowledge Discovery and Data Mining (KDD), Data Mining and Healthcare (DMH) (2013)
Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, pp. 324–331. IEEE (2009)
Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decis. Mak. 5(04), 597–604 (2006)
Zheng, B., Zhang, J., Yoon, S.W., Lam, S.S., Khasawneh, M., Poranki, S.: Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Syst. Appl. 42(20), 7110–7120 (2015)
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., Graña, M., Beristain, A., Ríos, S. (2017). Emergency Department Readmission Risk Prediction: A Case Study in Chile. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-59773-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59772-0
Online ISBN: 978-3-319-59773-7
eBook Packages: Computer ScienceComputer Science (R0)