Readmission prediction using trajectory-based deep learning approach

Jiaheng Xie, Bin Zhang, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2018, Proceedings
EditorsHsinchun Chen, Daniel Zeng, Qing Fang, Jiang Wu
Number of pages7
ISBN (Print)9783030036485
StatePublished - 2018
EventInternational Conference on Smart Health, ICSH 2018 - Wuhan, China
Duration: Jul 1 2018Jul 3 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10983 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Smart Health, ICSH 2018


  • Deep learning
  • Design science
  • Health IT
  • Hospital readmission
  • Predictive analytics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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