Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.

This paper introduces the informatization construction of the world's first non-military Type 3 international emergency medical team (EMT Type3), which was established by West China Hospital of Sichuan University. Based on the requirements of World Health Organization (WHO) for EMT, the team built a complete information-based emergency rescue platform from the network layout, the design of software and hardware facilities and other aspects, combined with their own circumstance, which has improved the communication ability effectively, and has passed the expert certification of the WHO smoothly.

The paper introduces the current situation of hospital operation index statistics,explores the design of hospital operation index set based on the standards,an index system with 3-layer architecture based on the data platform and the application modules of the index set.The development and application of this system ensures the consistent calculation,highly efficient utilization and shared reuse of statistical indexes and makes the hospital data services more orderly,efficient and accurate.

The paper introduces technologies related to data mining , including the feature selection , outlier detection model , cluste-ring model, association rule model, classification model, ensemble learning algorithm, etc.It makes detailed explanation of the applica-tion of data mining in the diagnosis , prognosis and management of clinical malignant tumors .

Objective To understand the depressive status of patients with HIV/AIDS and their family members in order to provide objective information for conducting psychological interventions among them. Method The Self-rating Depression Scale (SDS) questionnaires were used for assessing the depressive status and the SPSS software was applied for data analysis. Results The SDS values among the patients with HIV/AIDS and the family members of died AIDS patients were significantly higher than those of healthy controls. The proportion of HIV/AIDS patients with medium to severe degree of depression was 15.5%, which was higher than that of healthy controls. The SDS values among female family members of HIV/AIDS patients were higher than those in male ones. Conclusion The depression is a negative psychological status usually occurred among the patients with HIV/AIDS and their family members, and the psychological interventions should be conducted based on the characteristics of different populations.