Computer methods and programs in biomedicine · 2019
A machine learning approach for predicting urine output after fluid administration.
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Lin Pei-Chen, Huang Hsu-Cheng, Komorowski Matthieu, Lin Wei-Kai, Chang Chun-Min, Chen Kuan-Ta, Li Yu-Chuan, Lin Ming-Chin
Аннотация
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.
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.