represents the patient-level SHAP analyses of the ML model running at admission and predicting AKI during the hospitalization

<p>(A) and the risk of AKD transition at day 1, 2, 3, and 4 (B-E). Model A predicted AKI (PRED = 1). Model B predicted no transition to AKD (PRED = 0). Models C-E predicted the transition to AKD (PRED = 1). Color legend: In red are the factors positively associated with the risk of the outcome...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Lorenzo Ruinelli (13014138) (author)
مؤلفون آخرون:	Pietro Cippà (12072691) (author), Chantal Sieber (21637054) (author), Clelia Di Serio (94300) (author), Paolo Ferrari (12072688) (author), Antonio Bellasi (3756271) (author)
منشور في:	2025
الموضوعات:	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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represents the patient-level SHAP analyses of the ML model running at admission and predicting AKI during the hospitalization

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