A machine learning approach for predicting urine output after fluid administration

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題名:	A machine learning approach for predicting urine output after fluid administration
作者:	林明錦 Pei-Chen Lin, Hsu-Cheng Huang, Matthieu Komorowski, Wei-Kai Lin, Chun-Min Chang, Kuan-Ta Chen, Yu-Chuan Li, Ming-Chin Lin
貢獻者:	醫學資訊研究所
日期:	2019-08
上傳時間:	2025-04-02 15:03:43 (UTC+8)
摘要:	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.
關聯:	Comput Methods Programs Biomed. 2019 Aug; 177: 155-159
描述:	【109-2 升等】臺北醫學大學教師升等專門著作職別：專任送審等級：副教授著作送審
資料類型:	article
顯示於類別:	[教師升等送審著作] 109

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