Joint prediction scheme of pulmonary infection risk after renal transplantation

Bo Chen; Qianxue Yang; Luping Ji

doi:10.1117/12.2687552

28 August 2023 Joint prediction scheme of pulmonary infection risk after renal transplantation

Bo Chen, Qianxue Yang, Luping Ji

Author Affiliations +

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272404 (2023) https://doi.org/10.1117/12.2687552
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Postoperative infection is clinically considered to be the leading cause of patient death within one year after kidney transplantation. Pulmonary infection accounts for a large proportion. The factors that cause lung infection after kidney transplantation are so complex that clinicians have difficulty in determining whether a patient will be infected. Existing methods are to statically predict whether the patient will be infected by using the various indicators of a patient before surgery. Aiming to efficiently predict the infection risk of patients at any time, this paper proposes a joint prediction method based on recurrent neural network. We collect 656 real clinical data of kidney transplantation patients, including the relevant index data of patients before kidney transplant operation and the index data of regular testing after operation. Firstly, it pre-processes and cleans clinical patient data. Then, it adopts a multi-layer perceptron (MLP) to extract the static features of preoperative data, and a GRU network to extract the features of postoperative examination at each time sequence node. Finally, a residual connection is used to fuse these two kinds of features extracted respectively from the preoperative and postoperative examination data. The fused features, containing both semantic information and temporal information, are fed into a neural classifier to predict the pulmonary infection risks of patients. The experiments with performance comparisons verify that our method is effective. Hope our method can help clinicians to predict postoperative infection in advance.

Citation Download Citation

Bo Chen, Qianxue Yang, and Luping Ji "Joint prediction scheme of pulmonary infection risk after renal transplantation", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272404 (28 August 2023); https://doi.org/10.1117/12.2687552

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KEYWORDS

Transplantation

Feature extraction

Data modeling

Surgery

Kidney

Education and training

Neural networks

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