Objective To construct a hypertension structured database based on Yi-9B large language model by aiming at the large amount of unstructured data generated in the process of hypertension diagnosis and treatment in order to elevate the efficiency of data management and provide the support for clinical deci-sion-making.Methods The key clinical informations of 114 369 patients with hypertension visiting in the Sec-ond Affiliated Hospital of Army Medical University during 2014-2023 were extracted.The Yi-9B large lan-guage model was used for conducting the entity identification and data structuring,and the database architec-ture was designed for statistical analysis and clinical application.Results After the database structuring process,the mean values of systolic and diastolic blood pressure were(149.98±20.55)mmHg and(86.90±13.75)mmHg,respectively.According to the classification of blood pressure level,the proportions of the nor-mal high value for high risk,very high risk of hypertension grade 1,and very high risk of hypertension grade 2 were the highest,which accounted for 20.73％,27.80％and 19.59％respectively.52.64％of the patients were complicated with heart disease,10.18％with complicating diabetes and 12.71％with complicating hy-perlipidemia.Logistic regression analysis showed that＞50-60 and＞60-70 years old was the high incidence age segment,moreover the systolic blood pressure showed an increasing trend with the age increase,reflecting the universality of hypertension in aging.This database significantly improved the efficiency of diagnosis and treatment in clinical application and realized the efficient analysis and management of data.Conclusion The hyper-tension structured database based on Yi-9B large language model effectively processes the unstructured data,significantly improves the efficiency of data extraction and management,helps to optimize the diagnosis and treatment decision-making,improves the management efficiency and provides the support for intelligent man-agement and personalized diagnosis and treatment.

Objective To construct the predictive model of pressure injury(PI)in the patients with chronic pain based on machine learning,and to analyze its accuracy and rationality,so as to provide an evidence for the predictive evaluation of clinical PI.Methods The clinical medical records data of 396 patients with chronic pain and high risk Braden scores hospitalized in a class 3A hospital of Chongqing City from March 2023 to June 2024 were retrospectively analyzed.Based on the Python3.10 programming language,the decision tree model,random forest model,linear regression model,naive Bayes model and K-Means model were con-structed,and the model performances were compared by accuracy,sensitivity,precision,F1 score and area un-der the receiver operating characteristic(ROC)curve(AUC).Results PI occurred in 35 cases with an inci-dence rate of 8.84%.Age,NRS score,pain site and pain affected sleep were the independent influencing fac-tors for the PI occurrence in the patients with chronic pain.Among 5 kinds of PI risk predictive model,the ac-curacy(0.873),sensitivity(0.874),precision(0.848),F1 score(0.844)and ROC AUC(0.81)of the ran-dom forest model were all higher than those of other models.Conclusion The random forest model has a high predictive performance for PI in the patients with chronic pain,and could be used for the screening and man-agement of high risk groups of PI in the patients with chronic pain.

Objective To study the hemodynamic effects of enhanced external counter pulsation(EECP)on typical coronary artery disease and microcirculation angina.Methods A physiological model of the right dominant coronary artery,including the coronary conduit arteries and coronary microcirculation,was established using lumped parameter models.Pathological conditions,such as one-vessel lesions,three-vessel lesions,and microcirculation angina,were simulated.EECP intervention models were established,and the hemodynamic effects of EECP on pathological models was simulated.Results The simulation results of the coronary physiological model,pathological models,and EECP intervention model established in this study were consistent with experimental data in related literature.EECP improved coronary blood flow in all three pathological conditions.For one-vessel lesions,EECP could not recover the blood flow of left main coronary artery to a normal level after the stenosis rate reached 80%-85%.For three-vessel lesions,EECP treatment could not be used if the stenosis rate in one of the three vessels exceeded 90%.For microcirculation angina,EECP was effective when critical condition myocardial blood flow was>1.03 mL/min·g and coronary flow reserve was>1.64.Conclusions The model of coronary disease under EECP interference established in this study meets expectations,and the obtained simulation data have certain reference values for the clinical application of EECP.