Demographic, administrative, and laboratory data of the entire study cohort (utilized for Aim 1).

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Bibliographic Details
Main Author:	Lorenzo Ruinelli (13014138) (author)
Other Authors:	Pietro Cippà (12072691) (author), Chantal Sieber (21637054) (author), Clelia Di Serio (94300) (author), Paolo Ferrari (12072688) (author), Antonio Bellasi (3756271) (author)
Published:	2025
Subjects:	Medicine Biotechnology Developmental Biology Marine Biology Virology Computational Biology negative predictive value individualizing patient care electronic health records acute kidney injury acute kidney disease renal function trajectory baseline renal function public hospital network increased cost burden urgent hospital admission integrating ehr accumulated first three days roc 79 %) xlink "> ml akd complicated 10 xlink "> hospital admission first days renal failure progressively increased hospital observation 88 %) first day sometimes recognized relevant predictor reduced outcomes promptly treated patients admitted ml accuracy machine learning international guidelines frequent complications fold cross females ). ehr ), diagnostic accuracy defined according artificial intelligence analyzed 34 60 years
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Demographic, administrative, and laboratory data of the entire study cohort (utilized for <i>Aim 1</i>).