Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performan...

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Veröffentlicht in:	Artificial intelligence in medicine 2024-03, Vol.149, p.102785, Article 102785
Hauptverfasser:	Yang, Meicheng, Liu, Songqiao, Hao, Tong, Ma, Caiyun, Chen, Hui, Li, Yuwen, Wu, Changde, Xie, Jianfeng, Qiu, Haibo, Li, Jianqing, Yang, Yi, Liu, Chengyu
Format:	Artikel
Sprache:	eng
Schlagworte:	Acute kidney injury Acute Kidney Injury - diagnosis Acute Kidney Injury - etiology Critical care Critical Illness External validation Humans Model interpretation Patients Predictive model Risk Assessment Risk Factors
Online-Zugang:	Volltext
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