Analyzing 30-day readmission rate for heart failure using different predictive models

Satish Mahajan, Prabir Burman, Michael Hogarth

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

12 Scopus citations


The Center for Medicare and Medical Services in the United States compares hospital's readmission performance to the facilities across the nation using a 30-day window from the hospital discharge. Heart Failure (HF) is one of the conditions included in the comparison, as it is the most frequent and the most expensive diagnosis for hospitalization. If risk stratification for readmission of HF patients could be carried out at the time of discharge from the index hospitalization, corresponding appropriate post-discharge interventions could be arranged. We, therefore, sought to compare two different risk prediction models using 48 clinical predictors from electronic health records data of 1037 HF patients from one hospital. We used logistic regression and random forest as methods of analyses and found that logistic regression with bagging approach produced better predictive results (C-Statistics: 0.65) when compared to random forest (C-Statistics: 0.61).

Original languageEnglish (US)
Title of host publicationNursing Informatics 2016 - eHealth for All: Every Level Collaboration - From Project to Realization
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614996576
StatePublished - 2016
Event13th International Conference on Nursing Informatics, NI 2016 - Geneva, Switzerland
Duration: Jun 25 2016Jun 29 2016

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)09269630
ISSN (Electronic)18798365


Other13th International Conference on Nursing Informatics, NI 2016


  • Electronic health records
  • Heart Failure
  • Logistic regression
  • Predictive models
  • Random forest
  • Readmission

ASJC Scopus subject areas

  • Medicine(all)
  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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