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heart failure, natural language processing, nursing



  1. Kang, Youjeong PhD, MPH, CCRN
  2. Topaz, Maxim PhD, RN, MA
  3. Dunbar, Sandra B. RN, PhD, FAAN, FAHA, FPCNA
  4. Stehlik, Josef MD, MPH
  5. Hurdle, John MD, PhD


Background: For patients with heart failure (HF), there have been efforts to reduce the risk of 30-day rehospitalization, such as developing predictive models using electronic health records. Few previous studies used clinical notes to predict 30-day rehospitalization.


Objective: The aim of this study was to assess the utility of nursing notes versus discharge summaries to predict 30-day rehospitalization among patients with HF.


Methods: In this pilot study, we used free-text discharge summaries and nursing notes collected from a tertiary hospital. We randomly selected 500 Medicare patients with HF. We followed the natural language processing and machine learning pipeline for data analysis.


Results: Thirty-day rehospitalization risk prediction using discharge summaries (n = 500) produced an area under the receiver operating characteristic curve of 0.74 (Bag of Words + Neural Network). Thirty-day rehospitalization risk prediction using nursing notes (n = 2046) resulted in an area under the receiver operating characteristic curve of 0.85 (Bag of Words + Neural Network).


Conclusion: Nursing notes provide a superior input to risk models for 30-day rehospitalization in Medicare patients with HF compared with discharge summaries.