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Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study

Our model improves performance up to an AUC of 0.97 and an overall average of 0.92, which outperforms previous studies showing AKI prediction with RNN-based methods by Kim et al [43] in hospitalized patients (AUC 0.927), Rank et al [45] in cardiac surgery patients (AUC 0.893), and Xu et al [44] in inpatients (AUC 0.908). These results show promise for our model as a tool to predict AKI and facilitate early intervention and mitigation strategies for patients.

Suncheol Heo, Eun-Ae Kang, Jae Yong Yu, Hae Reong Kim, Suehyun Lee, Kwangsoo Kim, Yul Hwangbo, Rae Woong Park, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Hyojung Jung, Yebin Chegal, Jae-Hyun Lee, Yu Rang Park

JMIR Med Inform 2024;12:e47693