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BioMed Research International
Volume 2019, Article ID 8532892, 9 pages
https://doi.org/10.1155/2019/8532892
Research Article

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

1Research Center on Business Intelligence, University of Chile, Beauchef 851, Of. 502, Santiago, Chile
2Hospital Dr. Exequiel González Cortés, Gran Avenida 3300, San Miguel, Santiago, Chile
3Computation Intelligence Group, Basque University (UPV/EHU) P. Manuel Lardizabal 1, 20018 San Sebastian, Spain
4ACPySS, San Sebastián, Spain

Correspondence should be addressed to Manuel Graña; sue.uhe@anarg.leunam

Received 21 December 2018; Revised 8 March 2019; Accepted 1 April 2019; Published 15 April 2019

Academic Editor: Xudong Huang

Copyright © 2019 Patricio Wolff et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Kansagara, H. Englander, A. Salanitro et al., “Risk prediction models for hospital readmission: a systematic review,” The Journal of the American Medical Association, vol. 306, no. 15, pp. 1688–1698, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. M. M. Nakamura, S. L. Toomey, A. M. Zaslavsky et al., “Measuring pediatric hospital readmission rates to drive quality improvement,” Academic Pediatrics, vol. 14, no. 5, pp. S39–S46, 2014, Advances in Children’s Healthcare Quality: The Pediatric Quality Measures Program. View at Publisher · View at Google Scholar · View at Scopus
  3. D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, and G. Escobar, “Big data in health care: using analytics to identify and manage high-risk and high-cost patients,” Health Affairs, vol. 33, no. 7, pp. 1123–1131, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Futoma, J. Morris, and J. Lucas, “A comparison of models for predicting early hospital readmissions,” Journal of Biomedical Informatics, vol. 56, pp. 229–238, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Shameer, K. W. Johnson, A. Yahi et al., “Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: A case-study using mount sinai heart failure cohort,” in Proceedings of the 22nd Pacific Symposium on Biocomputing, (PSB '17), pp. 276–287, USA, January 2017. View at Scopus
  6. A. Artetxe, A. Beristain, and M. Graña, “Predictive models for hospital readmission risk: A systematic review of methods,” Computer Methods and Programs in Biomedicine, vol. 164, pp. 49–64, 2018. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Garmendia, M. Graña, J. M. Lopez-Guede, and S. Rios, “Predicting patient hospitalization after emergency readmission,” Cybernetics and Systems, vol. 48, no. 3, pp. 182–192, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. K. J. Ottenbacher, P. M. Smith, S. B. Illig, R. T. Linn, R. C. Fiedler, and C. V. Granger, “Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke,” Journal of Clinical Epidemiology, vol. 54, no. 11, pp. 1159–1165, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Garmendia, M. Graña, J. Lopez Guede, and S. Rios, “Neural and statistical predictors for time to readmission in emergency departments: a case study,” Neurocomputing, 2019. View at Google Scholar
  10. A. Artetxe, M. Graña, A. Beristain, and S. Ríos, “Balanced training of a hybrid ensemble method for imbalanced datasets: a case of emergency department readmission prediction,” Neural Computing and Applications, pp. 1–10, 2017. View at Google Scholar · View at Scopus
  11. B. Zheng, J. Zhang, S. W. Yoon, S. S. Lam, M. Khasawneh, and S. Poranki, “Predictive modeling of hospital readmissions using metaheuristics and data mining,” Expert Systems with Applications, vol. 42, no. 20, pp. 7110–7120, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Cui, D. Wang, Y. Wang, P.-W. Yu, and Y. Jin, “An improved support vector machine-based diabetic readmission prediction,” Computer Methods and Programs in Biomedicine, vol. 166, pp. 123–135, 2018. View at Publisher · View at Google Scholar · View at Scopus
  13. B. K. Reddy and D. Delen, “Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology,” Computers in Biology and Medicine, vol. 101, pp. 199–209, 2018. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Xiao, T. Ma, A. B. Dieng, D. M. Blei, and F. Wang, “Readmission prediction via deep contextual embedding of clinical concepts,” PLoS ONE, vol. 13, no. 4, pp. 1–15, 2018. View at Google Scholar · View at Scopus
  15. M. Vukicevic, S. Radovanovic, A. Kovacevic, G. Stiglic, and Z. Obradovic, “Improving hospital readmission prediction using domain knowledge based virtual examples,” Lecture Notes in Business Information Processing, vol. 224, pp. 695–706, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Artetxe, B. Ayerdi, M. Graña, and S. Rios, “Using Anticipative Hybrid Extreme Rotation Forest to predict emergency service readmission risk,” Journal of Computational Science, vol. 20, pp. 154–161, 2017. View at Google Scholar · View at Scopus
  17. I. Bergese, S. Frigerio, M. Clari et al., “An innovative model to predict pediatric emergency department return visits,” Pediatric Emergency Care, 2016. View at Google Scholar · View at Scopus
  18. H. Kaur, J. M. Naessens, A. C. Hanson, K. Fryer, M. E. Nemergut, and S. Tripathi, “PROPER: development of an early pediatric intensive care unit readmission risk prediction tool,” Journal of Intensive Care Medicine, vol. 33, no. 1, pp. 29–36, 2018. View at Publisher · View at Google Scholar · View at Scopus
  19. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Wolff, M. Graña, S. Rios, M. B. Yarza, and M. Graña, “RapidMiner code for a pediatric case of readmission risk modeling,” 2019, https://doi.org/10.5281/zenodo.2597686. View at Publisher · View at Google Scholar
  21. R. B. Morse, M. Hall, E. S. Fieldston et al., “Children's hospitals with shorter lengths of stay do not have higher readmission rates,” Journal of Pediatrics, vol. 163, no. 4, pp. 1034–1038, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, USA, 3rd edition, 2011.
  23. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, 1998.
  24. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, Mass, USA, 2016. View at MathSciNet
  25. V. N. Vapnik, “Statistical learning theory,” Adaptive and learning Systems for Signal Processing, Communications and Control, vol. 2, pp. 1–740, 1998. View at Google Scholar · View at MathSciNet
  26. B. Schölkopf, “Learning with kernels,” Journal of the Electrochemical Society, vol. 129, p. 2865, 2002. View at Publisher · View at Google Scholar
  27. C. Chang and C. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–39, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Ruder, “An overview of gradient descent optimization algorithms,” 2016, https://arxiv.org/abs/1609.04747.
  29. M. D. Zeiler, “ADADELTA: an adaptive learning rate method,” 2012, https://arxiv.org/abs/1212.5701.
  30. D. Cook, Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI, O’Reilly Media, 2016.
  31. N. S. Bardach, E. Vittinghoff, R. Asteria-Peñaloza et al., “Measuring hospital quality using pediatric readmission and revisit rates,” Pediatrics, vol. 132, no. 3, pp. 429–436, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Pandey, H. Golwala, H. Xu et al., “Association of 30-day readmission metric for heart failure under the hospital readmissions reduction program with quality of care and outcomes,” JACC: Heart Failure, vol. 4, no. 12, pp. 935–946, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. K. A. Auger, E. L. Mueller, S. H. Weinberg et al., “A validated method for identifying unplanned pediatric readmission,” Journal of Pediatrics, vol. 170, pp. 105–112.e2, 2016. View at Publisher · View at Google Scholar · View at Scopus
  34. J. G. Berry, D. E. Hall, D. Z. Kuo et al., “Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals,” Journal of the American Medical Association, vol. 305, no. 7, pp. 682–690, 2011. View at Publisher · View at Google Scholar
  35. R. Flippo, E. NeSmith, N. Stark, T. Joshua, and M. Hoehn, “Reduction of 30-day preventable pediatric readmission rates with postdischarge phone calls utilizing a patient- and family-centered care approach,” Journal of Pediatric Health Care, vol. 29, no. 6, pp. 492–500, 2015. View at Publisher · View at Google Scholar · View at Scopus
  36. K. Parikh, J. Berry, M. Hall et al., “Racial and ethnic differences in pediatric readmissions for common chronic conditions,” Journal of Pediatrics, vol. 186, pp. 158–164.e1, 2017. View at Publisher · View at Google Scholar · View at Scopus
  37. E. M. Bucholz, J. C. Gay, M. Hall, M. Harris, and J. G. Berry, “Timing and causes of common pediatric readmissions,” Journal of Pediatrics, vol. 200, pp. 240–248.e1, 2018. View at Publisher · View at Google Scholar · View at Scopus
  38. J. E. McMillan, E. R. Meier, J. C. Winer et al., “Clinical and geographic characterization of 30-day readmissions in pediatric sickle cell crisis patients,” Hospital Pediatrics, vol. 5, no. 8, pp. 423–431, 2015. View at Publisher · View at Google Scholar
  39. J. C. Gay, R. Agrawal, K. A. Auger et al., “Rates and impact of potentially preventable readmissions at children's hospitals,” Journal of Pediatrics, vol. 166, no. 3, pp. 613–619.e5, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. G. M. Weiss, “Mining with rarity: a unifying framework,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 7–19, 2004. View at Publisher · View at Google Scholar
  41. A. Besga, B. Ayerdi, G. Alcalde et al., “Risk factors for emergency department short time readmission in stratified population,” BioMed Research International, vol. 2015, Article ID 685067, 7 pages, 2015. View at Google Scholar · View at Scopus