This paper summarizes the experience of team training mode in nursing safety management.①Train key members,using case exercises of simulated teaching method and cross-departmental、cross-disciplinary;②The key members of nursing is responsible for the training of the medical staff,and as an observer in the department, observe the practical application of the team training mode;③Build team structure and master four important skills (communication,leadership,monitoring and mutual assistance);④Apply team training mode to nursing safety man-agement. After practice of one year,compare the incidence of safety changes of the observers and nursing adverse events before and after the training. In nursing staff cognition,the changes of team composition with leadership,vigi-lance,cooperation and effective communication,it is statistically significant for the team collaboration,security atmo-sphere,management perception,stress perception,and job satisfaction in the safety attitude scale(P<0.05),fell by 31.3％in the incidence of nursing adverse events. It is believed that the team training mode can improve the knowledge, attitude and execution of the nurses,reduce the occurrence of nursing adverse events,and ensure the patient's safety.

Objective:To explore the value of different radiomics models based on machine learning in predicting the risk of distant recurrence and metastasis of triple-negative breast cancer after neoadjuvant therapy.Methods:The clinical and imaging data of 150 patients with triple-negative breast cancer (TNBC) confirmed by histopathology were retrospectively analyzed. All patients underwent neoadjuvant chemotherapy and surgical resection from August 2011 to May 2017 in Fudan University Shanghai Cancer Center and Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. One hundred and nine patients from Shanghai Fudan University Shanghai Cancer Center were used as the training group, and 41 patients from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine were used as the validation group. The features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) before treatment and were added with time domain features innovatively. Least absolute shrinkage and selection operator cross validation and recursive feature elimination were applied to select features. Six different supervised machine learning algorithms (logistic regression, linear discriminant analysis, k-nearest neighbor, naive bayesian, decision tree, support vector machine) were used to predict the prognosis. ROC curve, accuracy and F1 measure were used to evaluate the performance of the six algorithms, and also verified by the validation group.Results:The support vector machine algorithm had the best predictive effect in the recurrence and metastasis model based on 15 features, with the highest area under curve (training group was 0.917, validation group was 0.859), and the highest accuracy rate (training group was 87.5%, validation group was 82.9%) and the highest F1 measure (training group was 0.800, validation group was 0.741). In addition, of the 15 imaging features, 12 were the time domain features and 3 were spatial features.Conclusion:With the help of the time domain features and machine learning algorithms, radiomics signatures based on preoperative DCE-MRI can help predict the distant prognosis for TNBC after neoadjuvant chemotherapy and provide support for clinical decision making and follow-up management.