Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan

PURPOSE: To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan. METHODS: This study used data from the Critical Care Databas...

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Detalles Bibliográficos
Autores principales: Huang, Chun-Te, Wang, Tsai-Jung, Kuo, Li-Kuo, Tsai, Ming-Ju, Cia, Cong-Tat, Chiang, Dung-Hung, Chang, Po-Jen, Chong, Inn-Wen, Tsai, Yi-Shan, Chu, Yuan-Chia, Liu, Chia-Jen, Chen, Cheng-Hsu, Pai, Kai-Chih, Wu, Chieh-Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562351/
https://www.ncbi.nlm.nih.gov/pubmed/37822805
http://dx.doi.org/10.1007/s13755-023-00248-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562351/
https://www.ncbi.nlm.nih.gov/pubmed/37822805
http://dx.doi.org/10.1007/s13755-023-00248-5

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