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A machine learning approach for predicting urine output after fluid administration.

Pei-Chen Lin1, Hsu-Cheng Huang2, Matthieu Komorowski3

  • 1Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; Emergency Department, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan.

Computer Methods and Programs in Biomedicine
|July 20, 2019
PubMed
Summary

Machine learning accurately predicts changes in urine output for sepsis patients after fluid resuscitation. This tool aids clinicians in managing fluid status and preventing complications.

Keywords:
Clinical decision supportElectronic health recordsFluid resuscitationMachine learningPredictionSepsis

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Area of Science:

  • Critical Care Medicine
  • Machine Learning in Healthcare
  • Renal Physiology

Background:

  • Sepsis management involves careful fluid resuscitation.
  • Monitoring urine output (UO) is crucial for assessing fluid status.
  • Predicting UO changes aids in preventing fluid overload complications.

Purpose of the Study:

  • Develop a machine learning model to predict urine output in sepsis patients post-fluid resuscitation.
  • Identify patients at risk of decreased UO or oliguria.

Main Methods:

  • Utilized the eXtreme Gradient Boosting algorithm on the MIMIC-III v1.4 database.
  • Included sepsis patients (Sepsis-3 criteria).
  • Focused on predicting decreased UO and oliguria (UO < 0.5 mL/kg/h).

Main Results:

  • Model achieved an AUC of 0.86 for predicting decreased UO.
  • Models for oliguria prediction showed AUC > 0.86.
  • Highest sensitivity for oliguria prediction reached 92.2% in patients with baseline oliguria.

Conclusions:

  • Machine learning models can effectively predict urine output in sepsis.
  • These models assist clinicians in evaluating fluid status.
  • Prediction aids in preventing fluid overload-related complications.