Readmission Prediction Using Trajectory-Based Deep Learning Approach

  • Jiaheng XieEmail author
  • Bin Zhang
  • Daniel Zeng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)


Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis after discharge. It causes $26 billion preventable expense to the U.S. health systems annually and may indicate suboptimal care for patients. Predicting readmission risk is essential to alleviate such financial and medical consequences. Yet such prediction is challenging due to the dynamic and complex nature of the hospitalization trajectory. The state-of-the-art studies apply statistical models with unified parameters for all patients and use static predictors in a period, failing to consider patients’ heterogeneous illness trajectories. Our approach – TADEL (Trajectory-BAsed DEep Learning) – addresses the present challenge and captures various illness trajectories. We evaluate TADEL on a unique five-year national Medicare claims dataset, reaching a precision of 0.780, a recall of 0.985, and an F1-score of 0.870. This study contributes to IS literature and methodology by formulating the readmission prediction problem and developing a novel personalized readmission risk prediction framework. This framework provides direct implications for health providers to assess patients’ readmission risk and take early interventions to avoid potential negative consequences.


Hospital readmission Predictive analytics Deep learning Health IT Design science 


  1. Axon, R.N., Williams, M.V.: Hospital readmission as an accountability measure. JAMA 305(5), 504 (2011)CrossRefGoogle Scholar
  2. Bardhan, I., Oh, J.H.C., Zheng, Z.E., Kirksey, K.: Predictive analytics for readmission of patients with congestive heart failure. Inf. Syst. Res. 26(1), 19–39 (2015)CrossRefGoogle Scholar
  3. Bottle, A., Aylin, P., Majeed, A.: Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J. R. Soc. Med. 99(8), 406–414 (2006)CrossRefGoogle Scholar
  4. Brännström, J., Sönnerborg, A., Svedhem, V., Neogi, U., Marrone, G.: A high rate of HIV-1 acquisition post immigration among migrants in Sweden determined by a CD4 T-cell decline trajectory model. HIV Med. 18(9), 677–684 (2017)CrossRefGoogle Scholar
  5. Center for Health Information and Analysis: Performance of the Massachusetts Health Care System Series: A Focus on Provider Quality (2015)Google Scholar
  6. Chen, R., et al.: Cloud-based predictive modeling system and its application to asthma readmission prediction. In: AMIA Annual Symposium Proceedings. AMIA Symposium vol. 2015, pp. 406–415 (2015)Google Scholar
  7. Coleman, E.A., Min, S.J., Chomiak, A., Kramer, A.M.: Posthospital care transitions: patterns, complications, and risk identification. Health Serv. Res. 39(5), 1449–1466 (2004)CrossRefGoogle Scholar
  8. Corbin, J.M., Strauss, A.: A nursing model for chronic illness management based upon the trajectory framework. Sch. Inq. Nurs. Pract. 5, 155–1774 (1991)Google Scholar
  9. Dhalla, I.A., et al.: Effect of a postdischarge virtual ward on readmission or death for high-risk patients. JAMA 312(13), 1305 (2014)CrossRefGoogle Scholar
  10. Donnelly, C., McFetridge, L.M., Marshall, A.H., Mitchell, H.J.: A two-stage approach to the joint analysis of longitudinal and survival data utilising the Coxian phase-type distribution. Stat. Methods Med. Res. (2017)
  11. Glance, L.G., et al.: Hospital readmission after noncardiac surgery. JAMA Surg. 149(5), 439 (2014)CrossRefGoogle Scholar
  12. Halfon, P., Eggli, Y., Pêtre-Rohrbach, I., Meylan, D., Marazzi, A., Burnand, B.: Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med. Care 44(11), 972–981 (2006)CrossRefGoogle Scholar
  13. Hammill, B.G., et al.: Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization. Circ. Cardiovasc. Qual. Outcomes 4(1), 60–67 (2011)CrossRefGoogle Scholar
  14. Hasan, O., et al.: Hospital readmission in general medicine patients: a prediction model. J. Gen. Intern. Med. 25(3), 211–219 (2010)CrossRefGoogle Scholar
  15. Holman, C.D.J., Preen, D.B., Baynham, N.J., Finn, J.C., Semmens, J.B.: A multipurpose comorbidity scoring system performed better than the Charlson index. J. Clin. Epidemiol. 58(10), 1006–1014 (2005)CrossRefGoogle Scholar
  16. Howell, S., Coory, M., Martin, J., Duckett, S.: Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv. Res. 9(1), 96 (2009)CrossRefGoogle Scholar
  17. Hser, Y.I., Longshore, D., Anglin, M.D.: The life course perspective on drug use. Eval. Rev. 31(6), 515–547 (2007)CrossRefGoogle Scholar
  18. Jovanovic, M., Radovanovic, S., Vukicevic, M., Van Poucke, S., Delibasic, B.: Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression. Artif. Intell. Med. 72, 12–21 (2016)CrossRefGoogle Scholar
  19. Kansagara, D., et al.: Risk prediction models for hospital readmission. JAMA 306(15), 1688 (2011)CrossRefGoogle Scholar
  20. Krumholz, H.M., et al.: Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission. Circ. Cardiovasc. Qual. Outcomes 2(5), 407–413 (2009)CrossRefGoogle Scholar
  21. Mishel, M.H.: Reconceptualization of the uncertainty in illness theory. Image J. Nurs. Scholarsh. 22(4), 256–262 (1990)CrossRefGoogle Scholar
  22. Paul, S.S., et al.: Two-year trajectory of fall risk in people with parkinson disease: a latent class analysis. Arch. Phys. Med. Rehabil. 97(3), 372–379.e1 (2016)CrossRefGoogle Scholar
  23. Radovanovic, S., Vukicevic, M., Kovacevic, A., Stiglic, G., Obradovic, Z.: Domain knowledge based hierarchical feature selection for 30-day hospital readmission prediction. In: Holmes, John H., Bellazzi, R., Sacchi, L., Peek, N. (eds.) AIME 2015. LNCS (LNAI), vol. 9105, pp. 96–100. Springer, Cham (2015). Scholar
  24. Shadmi, E., Flaks-Manov, N., Hoshen, M., Goldman, O., Bitterman, H., Balicer, R.D.: Predicting 30-day readmissions with preadmission electronic health record data. Med. Care 53(3), 283–289 (2015)CrossRefGoogle Scholar
  25. 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. Proc. (Bayl. Univ. Med. Cent.) 21(4), 363–372 (2008)CrossRefGoogle Scholar
  26. Vukicevic, M., Radovanovic, S., Kovacevic, A., Stiglic, G., Obradovic, Z.: Improving hospital readmission prediction using domain knowledge based virtual examples. In: Uden, L., Heričko, M., Ting, I.-H. (eds.) KMO 2015. LNBIP, vol. 224, pp. 695–706. Springer, Cham (2015). Scholar
  27. 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. CMAJ 182(6), 551–557 (2010)CrossRefGoogle Scholar
  28. Woog, P.: The Chronic Illness Trajectory Framework: The Corbin and Strauss Nursing Model. Springer Publishing Company, New York (1992)Google Scholar
  29. Xie, J., Liu, X., Zeng, D.D.: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation. J. Am. Med. Inform. Assoc. 25, 72–80 (2017)CrossRefGoogle Scholar
  30. Yu, S., Farooq, F., Esbroeck, A., Fung, G., Anand, V., Krishnapuram, B.: Predicting readmission risk with institution-specific prediction models. Artif. Intell. Med. 65(2), 89–96 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of ArizonaTucsonUSA

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