The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.

The aim of this work is to explore the working conditions of logistics personnel in hospital and provide a new approach for improving the service quality of them in the post-COVID-19 era. Eighty-four logistics personnel in an upper first-class hospital were included in the study, and their working conditions were investigated and analyzed via self-designed questionnaire. 73.8% of all respondents think their work is significantly important to the brand building of the hospital, and 67.9% of them think they are closely related to a harmonious doctor–patient relationship. The compliant rate is higher in security personnel when compared with other personnel and the difference was statistically significant ([Formula: see text]). 94% of the logistics personnel indicate a higher intensity in their work, 39.3% of them constantly face the working pressure and 57.1% come from the risk of infection which accounts for the largest. The largest demand comes for the protective equipment, which is from 69 workers. It would be better for us to pay attention to the hospital culture training and strengthen the sense of identity among logistics personnel of the hospital continuously. Meanwhile we need to enrich the connotation of professional style construction in hospital and promote the efficiency of logistics service. Targeted training is necessary to improve the service capabilities of the security personnel since they receive more complaints. Diversified and personalized support to the logistics personnel based on the grasp of their special demand is also important.