A machine learning approach for predicting urine output after fluid administration

Pei Chen Lin, Hsu Cheng Huang, Matthieu Komorowski, Wei Kai Lin, Chun Min Chang, Kuan Ta Chen, Yu Chuan Li, Ming Chin Lin

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Background and objective: To develop a machine learning model to predict urine output (UO)in sepsis patients after fluid resuscitation. Methods: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h)developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. Results: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC)of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. Conclusions: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Original languageEnglish
Pages (from-to)155-159
Number of pages5
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - Aug 1 2019


  • Clinical decision support
  • Electronic health records
  • Fluid resuscitation
  • Machine learning
  • Prediction
  • Sepsis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


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