ABSTRACT
OBJECTIVE@#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.@*METHODS@#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.@*RESULTS@#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. The predictive performances of the GRU and RNN models were significantly better than that of the LR model. The performances of the GRU and RNN models assessed by recall rate and F1-score were also significantly better than that of the LR model, in which the GRU model reached the best performance. In addition, the recall rates were different among different causes of death or by different prediction time windows.@*CONCLUSION@#The recurrent neural network model, especially the GRU model, is more effective in predicting PD patients' prognosis as compared with the LR model. This new model may be helpful for clinicians to provide timely intervention, thus improving the quality of care of PD.
Subject(s)
Humans , Databases, Genetic , Logistic Models , Neural Networks, Computer , Peritoneal Dialysis , PrognosisABSTRACT
<p><b>OBJECTIVE</b>To investigate the life style, genetic and occupational risk factors of metabolic syndrome (MS) among policemen.</p><p><b>METHODS</b>1:4 matched case-control study was used, based on physical examination data of Tianjin Policemen in 2010, 708 patients with MS were randomly selected as cases, which were matched with 2832 healthy controls on the basis of sex and age (+/- 1 year). An epidemiological investigations on the past exposure status of several possible risk factors was conducted, and the data were analyzed with conditional logistic regression.</p><p><b>RESULTS</b>Fifteen factors related to exposure were identified for MS through univariate conditional logistic regression analysis. Multivariate conditional logistic regression analysis suggested that, seven factors, such as family history of hypertension (OR = 2.406, 95% CI: 1.946-2.975), family history of diabetes (OR = 1.301, 95% CI: 1.043-1.623), smoking (OR = 1.357, 95%CI: 1.010-1.823), snoring (OR = 1.268, 95% CI: 1.043-1.543), work intensity (OR = 4.603, 95% CI: 3.767-5.623), occupational stressful events (OR = 1.524, 95% CI: 1.209-1.922), security policemen (OR = 1.453, 95% CI: 1.127-1.872) and criminal investigation policemen (OR = 2.792, 95% CI: 2.168-3.596), could significantly increase the risk of disease development, but dairy products (OR = 0.782, 95% CI: 0.619-0.989) was a protect factor for MS. The results from population attributable risk factors analysis showed that the control of smoking, snoring, work intensity, occupational stressful events can decreased the risk of MS to 16.26%, 11.71%, 56.87% and 8.97%, respectively.</p><p><b>CONCLUSION</b>Metabolic syndrome has became a significant public health problem among policemen, it's necessary to take measures on life style, occupational risk factors for reducing the incidence of MS, and improving the health level among policemen.</p>