Author:
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
Abstract
Abstract
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 Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established.
Results
The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers.
Conclusion
A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented.
Funder
the Taiwan Smart Healthcare Alliance and Ministry of Science and Technology
the Taiwan Smart Healthcare Alliance
the Ministry of Science and Technology
Publisher
Springer Science and Business Media LLC