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Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.

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Title: Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.
Authors: Ge, Yuanshuo1 (AUTHOR), Wang, Guangdong2 (AUTHOR), Zhang, Linlin3 (AUTHOR), Miao, Yang3 (AUTHOR), Wu, Hui3 (AUTHOR), Hu, Ye3 (AUTHOR) xhzwork@163.com, Yin, Cunlin3 (AUTHOR) yincunlin1988@163.com
Source: Science Progress. Apr-Jun2026, Vol. 109 Issue 2, p1-15. 15p.
Database: Academic Search Ultimate
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  Data: Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.
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  Data: <searchLink fieldCode="JN" term="%22Science+Progress%22">Science Progress</searchLink>. Apr-Jun2026, Vol. 109 Issue 2, p1-15. 15p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=194993328
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        Value: 10.1177/00368504261442370
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      – TitleFull: Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.
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              Text: Apr-Jun2026
              Type: published
              Y: 2026
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