Dynamic prediction of hospital admission with medical claim data

Tianzhong Yang, Yang Yang, Yugang Jia, Xiao Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Congestive heart failure is one of the most common reasons those aged 65 and over are hospitalized in the United States, which has caused a considerable economic burden. The precise prediction of hospitalization caused by congestive heart failure in the near future could prevent possible hospitalization, optimize the medical resources, and better meet the healthcare needs of patients. Methods: To fully utilize the monthly-updated claim feed data released by The Centers for Medicare and Medicaid Services (CMS), we present a dynamic random survival forest model adapted for periodically updated data to predict the risk of adverse events. We apply our model to dynamically predict the risk of hospital admission among patients with congestive heart failure identified using the Accountable Care Organization Operational System Claim and Claim Line Feed data from Feb 2014 to Sep 2015. We benchmark the proposed model with two commonly used models in medical application literature: the cox proportional model and logistic regression model with L-1 norm penalty. Results: Results show that our model has high Area-Under-the-ROC-Curve across time points and C-statistics. In addition to the high performance, it provides measures of variable importance and individual-level instant risk. Conclusion: We present an efficient model adapted for periodically updated data such as the monthly updated claim feed data released by CMS to predict the risk of hospitalization. In addition to processing big-volume periodically updated stream-like data, our model can capture event onset information and time-to-event information, incorporate time-varying features, provide insights of variable importance and have good prediction power. To the best of our knowledge, it is the first work combining sliding window technique with the random survival forest model. The model achieves remarkable performance and could be easily deployed to monitor patients in real time.

Original languageEnglish (US)
Article number18
JournalBMC medical informatics and decision making
StatePublished - Jan 31 2019
Externally publishedYes

Bibliographical note

Funding Information:
This research is generously supported by the Philips Research North America company fund for the project “Smart Hospital Network Analytics”. The fund supports activities including the study design, data storage, data analysis, client meeting and discussion, interpretation of data and the writing of the manuscript. This article did not receive sponsorship for publication, instead, publication of this article was funded by JY and YY.

Publisher Copyright:
© 2019 The Author(s).


  • Claim data
  • Congestive heart failure
  • Dynamic prediction
  • Hospitalization
  • Random survival forest
  • Sliding window
  • Survival analysis


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