illustrates the execution day and the number of patients used for each developed ML model.

<p>Model AKI_HOSP_T0 predicts whether an admitted patient will develop an AKI during their stay. This model is executed at admission time and is developed using the entire dataset (N = 34,579). Models AKD_T1-4 predict whether patients who experience AKI within the first 24 hours of hospitaliza...

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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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illustrates the execution day and the number of patients used for each developed ML model.

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