In order to improve medical students' cognition of neurosurgery specialty and clinical practice,and cultivate students' self-learning ability,the model of flipping classroom combined with problem based learning was applied in clinical teaching in our department.The experimental group adopted flip classroom combined with PBL which penetrated the pre-class teaching design,classroom activity design,after-class summary and teaching feedback,while the control group adopted the traditional teaching method.The evaluation results showed that the students in the experimental group had significantly higher scores in-class knowledge and examination results than those in the control group.In addition,students had a high degree of recognition and satisfaction with the newly combined teaching model.The combination of flipped classroom and PBL teaching method could make up their deficiency,complement each other to achieve the best clinical teaching effect and improve students comprehensive ability.Meanwhile it puts forward new requirements for students and teachers,during which teachers need to be fully prepared and update teaching concepts for the sake of fulfilling the mutual promotion of teaching and learning.

[Objective] To analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.