@article{9d60be7f688e4f37b6e51fb581986af9,
title = "A machine learning approach for predicting urine output after fluid administration",
abstract = "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.",
keywords = "Clinical decision support, Electronic health records, Fluid resuscitation, Machine learning, Prediction, Sepsis",
author = "Lin, {Pei Chen} and Huang, {Hsu Cheng} and Matthieu Komorowski and Lin, {Wei Kai} and Chang, {Chun Min} and Chen, {Kuan Ta} and Li, {Yu Chuan} and Lin, {Ming Chin}",
note = "Funding Information: We are grateful to Leo Celi from the Massachusetts Institute of Technology and Chin Lin from the National Defense Medical Center for their assistance with the methodology. We are thankful to the Laboratory of Computational Physiology at the Massachusetts Institute of Technology and the eICU Research Institute for providing the data used in this research. M.K. was funded by the Engineering and Physical Sciences Research Council and an Imperial College President's PhD Scholarship. This project was funded by Taipei Medical University (TMU103-AE1-B26). Funding Information: We are grateful to Leo Celi from the Massachusetts Institute of Technology and Chin Lin from the National Defense Medical Center for their assistance with the methodology. We are thankful to the Laboratory of Computational Physiology at the Massachusetts Institute of Technology and the eICU Research Institute for providing the data used in this research. M.K. was funded by the Engineering and Physical Sciences Research Council and an Imperial College President's PhD Scholarship. This project was funded by Taipei Medical University ( TMU103-AE1-B26 ). Publisher Copyright: {\textcopyright} 2019",
year = "2019",
month = aug,
day = "1",
doi = "10.1016/j.cmpb.2019.05.009",
language = "English",
volume = "177",
pages = "155--159",
journal = "Computer Methods and Programs in Biomedicine",
issn = "0169-2607",
publisher = "Elsevier Ireland Ltd",
}