Towards Hospitalization After Readmission Risk Prediction Using ELMs
A criteria to evaluate the performance of Emergency Departments (ED) is the number of readmissions and hospitalizations short time after discharge of patients because the problem was not solved in the first admission. Such events contribute to overload the care system and to worsening the health of patients. In this paper we address the problem of predicting hospitalization events after readmission in ED, facing it as a classification problem and using Extreme Learning Machines (ELM). We have carried out experiments with a dataset with 45,089 admission events of 21,269 pediatric patients recorded in the Hospital José Joaquín Aguirre of the University of Chile during 3 years and 4 months, improving the state-of-the-art sensitivity results on the same dataset by 17%.
KeywordsEmergency Department Extreme Learn Machine Minority Class Fragility Index Hospitalization Event
The research was supported by the Computational Intelligence Group of the Basque Country University (UPV/EHU) through Grant IT874-13 of Research Groups Call 2013–2017 (Basque Country Government).
- 1.Besga, A., Ayerdi, B., Alcalde, G., Manzano, A., Lopetegui, P., Graña, M., González-Pinto, A.: Risk factors for emergency department short time readmission in stratified population. BioMed Res. Int. 2015 (2015)Google Scholar
- 2.Nguyen, H.Q., Chu, L., Amy Liu, I.L., Lee, J.S., Suh, D., Korotzer, B., Yuen, G., Desai, S., Coleman, K.J., Xiang, A.H., Gould, M.K.: Associations between physical activity and 30-day readmission risk in chronic obstructive pulmonary disease. 1 Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California. FAU - Chu, LynnaGoogle Scholar
- 3.Pereira, L., Choquet, C., Perozziello, A., Wargon, M., Juillien, G., Colosi, L., Hellmann, R., Ranaivoson, M., Casalino, E.: Unscheduled-return-visits after an emergency department (ed) attendance and clinical link between both visits in patients aged 75 years and over: a prospective observational study. PLoS One 10(4), 1–13 (2015)Google Scholar
- 5.Hao, S., Wang, Y., Jin, B., Shin, A.Y., Zhu, C., Huang, M., Zheng, L., Luo, J., Hu, Z., Fu, C., Dai, D., Wang, Y., Culver, D.S., Alfreds, S.T., Rogow, T., Stearns, F., Sylvester, K.G., Widen, E., Ling, X.B.: Development, validation and deployment of a real time 30 day hospital readmission risk assessment tool in the maine healthcare information exchange. PLoS One 10(10), 1–15 (2015)Google Scholar
- 8.Silverstein, M.D., Qin, H., Mercer, S.Q., Fong, J., Haydar, Z.: Risk factors for 30-day hospital readmission in patients=65 years of age. In: Proceedings (Baylor University Medical Center), vol. 21, no. 4, pp. 363–72, October 2008. pT: J; TC: 62; UT: MEDLINE:18982076Google Scholar
- 9.Carpenter, C.R., Heard, K., Wilber, S., Ginde, A.A., Stiffler, K., Gerson, L.W., Wenger, N.S., Miller, D.K.: Research priorities for high-quality geriatric emergency care: medication management, screening, and prevention and functional assessment. Acad. Emerg. Med. 18(6), 644–654 (2011)CrossRefGoogle Scholar
- 10.Deschodt, M., Devriendt, E., Sabbe, M., Knockaert, D., Deboutte, P., Boonen, S., Flamaing, J., Milisen, K.: Characteristics of older adults admitted to the emergency department (ed) and their risk factors for ed readmission based on comprehensive geriatric assessment a prospective cohort study. BMC Geriatr. 15(1), 54 (2015). http://dx.doi.org/10.1186/s12877-015-0055-7 CrossRefGoogle Scholar
- 11.van Walraven, C., Dhalla, I.A., Bell, C., Etchells, E., Stiell, I.G., Zarnke, K., Austin, P.C., Forster, A.J.: 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). pT: J; TC: 144; UT: WOS:000275978300007CrossRefGoogle Scholar
- 12.van Walraven, C., Wong, J., Forster, A.J.: Lace+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med.: Peer-Rev. Indep. Open-Access J. 6(3), e80–e90 (2012). pT: J; TC: 15; UT: MEDLINE:23696773Google Scholar
- 13.van Walraven, C., McAlister, F.A., Bakal, J.A., Hawken, S., Donze, J.: External validation of the hospital-patient one-year mortality risk (homr) model for predicting death within 1 year after hospital admission. Can. Med. Assoc. J. 187(10), 725–733 (2015). pT: J; TC: 1; UT: WOS:000371005500009CrossRefGoogle Scholar
- 14.Garmendia, A., Graña, M., Lopez-Guede, J.M., Rios, S.: Predicting patient hospitalization after emergency readmission. Cybern. Syst. 48(3), 182–192 (2017). http://www.tandfonline.com/doi/abs/10.1080/01969722.2016.1276772 CrossRefGoogle Scholar
- 16.Haykin, S., Networks, N.: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River (1998)Google Scholar