AKI risk prediction model based on federated learning in medical big data

Junsong Wang; Shengwen Guo

doi:10.1117/12.2687741

28 August 2023 AKI risk prediction model based on federated learning in medical big data

Junsong Wang, Shengwen Guo

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272417 (2023) https://doi.org/10.1117/12.2687741
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Acute kidney injury (AKI) is a common critical syndrome with a high incidence rate and high mortality. The risk assessment and prediction of patients with AKI are of great significance for the timely intervention and treatment of patients with potential risks. Based on the large scale of clinical information of multi-center inpatients (46, 395 AKI inpatients vs. 534, 066 non-AKI inpatients), the distributed federal learning framework was applied to construct the AKI prediction models. Experimental results demonstrated that the proposed method achieved promising performance in AKI risk identification and prediction.

Citation Download Citation

Junsong Wang and Shengwen Guo "AKI risk prediction model based on federated learning in medical big data", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272417 (28 August 2023); https://doi.org/10.1117/12.2687741

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