External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

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作者： Francesca,Alfieri ^[1] ; Andrea,Ancona ^[1] ; Giovanni,Tripepi ^[2] ; Vincenzo,Randazzo ^[3] ; Annunziata,Paviglianiti ^[3] ; Eros,Pasero ^[3] ; Luigi,Vecchi ^[4] ; Cristina,Politi ^[2] ; Valentina,Cauda ^[5] ; Riccardo Maria,Fagugli ^[6]

作者单位： Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. ^[1] CNR-IFC, Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Nefrologia-Ospedali Riuniti, 89100, Reggio Calabria, Italy. ^[2] Department of Electronics and Telecommunications, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. ^[3] S.C. Nefrologia e Dialisi, Azienda Ospedaliera di Terni, viale Tristano di Joannuccio, 05100, Terni, Italy. ^[4] Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. valentina.cauda@polito.it. ^[5] S.C. Nefrologia e Dialisi, Azienda Ospedaliera di Terni, viale Tristano di Joannuccio, 05100, Terni, Italy. riccardomaria@fagugli.eu. ^[6]

关键词 Acute kidney injury Artificial intelligence KDIGO eAlert

主题词人类(Humans);危重病(Critical Illness);急性肾损伤(Acute Kidney Injury);重症监护病房(Intensive Care Units);少尿(Oliguria)

DOI 10.1007/s40620-022-01335-8

PMID 35554875

发布时间 2023-11-06

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