摘要
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.
原文 | 英語 |
---|---|
頁(從 - 到) | 155-159 |
頁數 | 5 |
期刊 | Computer Methods and Programs in Biomedicine |
卷 | 177 |
DOIs | |
出版狀態 | 已發佈 - 8月 1 2019 |
ASJC Scopus subject areas
- 軟體
- 電腦科學應用
- 健康資訊學