Jun 19, 2019

Application of recurrent neural network in prognosis of peritoneal dialysis

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
W TangY S Wang

Abstract

Deep learning models, including recurrent neural network (RNN) and gated recurrent unit (GRU), were used to construct the clinical prognostic prediction models for peritoneal dialysis (PD) patients based on routine clinical data. The performance of the RNN and GRU were compared with logistic regression (LR), which is commonly used in medical researches. The possible underlining clinical implications based on the result from the GRU model were also investigated. We used the clinical data from the PD center of Peking University Third Hospital as the data source. Both the baseline data at the beginning of dialysis, and the follow-up and prognostic data of the patients were used by the RNN and GRU prediction models. The hyper-parameters were tuned based on the 10-fold cross-validation. The risk prediction performance of each model was evaluated via area under the receiver operation characteristic curve (AUROC), recall rate and F1-score on the testset. A total of 656 patients with the 261 occurrences of death were included in the experiment. The total number of all diagnostic records were 13 091. The results on the testset showed that the AUROC of the LR model, RNN model, and GRU model was 0.701 4, 0.786 0, and 0.814 7, respectively...Continue Reading

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Mentioned in this Paper

Biological Neural Networks
Follow-up
Cessation of Life
Dialysis Procedure
Logistic Regression
Cross Validation
Clinician
Peritoneal
Hospitals, University
Lateral Root Formation

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