Predicting 120-day hospital readmission using medical administrative patient data

Marijn Van Wingerden*, Jelle De Boer, Eric Postma

*Corresponding author for this work

Research output: Contribution to journalConference articleOther research output


Hospitals and health-care insurers routinely use models to predict patient readmission, extrapolating from historical data. Subsequently, the predicted quantities can be used for contracting and pricing negotiations between these hospitals and healthcare insurers. The Dutch healthcare system uses unique standardized Care Trajectories (so-called DBCs) for administration and billing of care. Here, we compared supervised machine learning methods on predicting 120-day readmission as an operationally significant metric. We used administrative patient data from 21 common Care Trajectories, in combination with demographic information. A lightGBM model using undersampling to tackle class imbalance yielded an AUROC score of 0.86 and provided the highest recall score (73.8%).

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2019
Event31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019


  • Administrative patient data
  • Care trajectory
  • Health analytics
  • Hospital readmission

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