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Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods. Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results. A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion. An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

Deep vein thrombosis (DVT) is a serious complication in patients with acute ischemic stroke (AIS). Early prediction of DVT could enable physicians to perform a proper prevention strategy. We analyzed the association of clinical and laboratory variables with DVT to evaluate the risk of DVT in patients after AIS. AIS patients admitted to the Changsha Central Hospital between January 2017 and December 2019 with length of stay in hospital ≥7 days were included. Clinical and laboratory variables for DVT at baseline were collected, and the diagnosis of DVT was confirmed by ultrasonography. Independent factors were developed by Multivariate logistic regression analysis. A total of 101 patients were included in the study. The in-hospital incidence of DVT after AIS was 19.8%(20/101). The average level of D-dimer when DVT detected was significant increased around 4-fold than that on admission ( P < .001). Pulmonary infection (odds ratio [OR] = 5.4, 95%CI:1.10–26.65, P = .037)) and increased muscle tone (OR = 0.11, 95%CI:0.02–0.58, P = .010) as independent relevant factors for DVT were confirmed. Pulmonary infection as a risk factor and increased muscle tone as a protective factor for DVT were identified in patients after AIS. The level of D-dimer which increased around 4-fold compared to the initial level could be an indicator for DVT occurrence.