Purpose/Significance To analyze relevant patented technologies,and to provide experience for medical and commercial health insurance institutions to break through the data interaction blocking point and construct a multi-level medical security system.Method/Process The patentometrics method is used to analyze the data interaction patent technology between medical and health insurance institutions from four aspects:time trend,regional distribution,category distribution and text clustering.Result/Conclusion China should pay attention to the role of data interaction in deepening cooperation between medical and insurance institutions,improve the declaration,authorization,application and protection of high-quality patents,pay attention to cross-border integration and technology-driven,and innovate patent layout to meet technological development and social needs.

Influenza imposes a significant disease burden on society and individuals annually, and influenza vaccination is considered a significant public health measure to prevent influenza and reduce influenza-related severe disease and death. The low influenza vaccination rate in China is partly due to certain factors affecting the willingness and behavior of individuals to receive them. Scientific research and targeted interventions on these factors can effectively improve the vaccination situation. Commonly used individual-level theoretical models for influenza vaccination behavior include the health belief model, protection motivation theory, and theory of planned behavior. This study reviews theoretical models commonly employed in researching influenza vaccination willingness and behavior. An overview of these practical applications and challenges models is presented to provide references for relevant research and intervention programs in China.

OBJECTIVE To provide reference for strengthening the standardized management of off-label drug use in cancer hospitals. METHODS The evaluation system for off-label drug use was established to standardize the application， approval， and filing process for off-label drug use in our hospital. The changes in off-label drug application quantity， proportion， disease category and drug category in our hospital were compared before （October 1st， 2021-September 30th， 2022） and after （October 1st， 2022- September 30th， 2023） the establishment of the evaluation system； drug items supported by high-level evidence screened by pharmacy department were analyzed statistically. RESULTS The number of off-label drug use applications in our hospital had gradually increased， from 306 pieces in the fourth quarter of 2021 to 3 828 pieces in the third quarter of 2023. In the year before the construction of the evaluation system， there were a total of 4 482 applications for off-label drug use， and in the year after the construction of the evaluation system， there were 11 840 applications for off-label drug use. After the construction of the evaluation system， the proportion of unregistered off-label drug use significantly decreased， compared to the same period last year （P＜0.05）. Among them， there were no unregistered applications for off-label drug use for digestive system tumors， head and neck tumors， and radioactive drugs； lymphoma， breast tumors，urogenital system tumors， cytotoxic drugs and new anti-tumor drugs all had a decrease of over 70% in unregistered off-label drug applications. Twenty-seven off-label drug use items related to 19 drugs supported by high-level evidence were screened by the pharmacy department of our hospital， among which 25 items were drug use beyond indication. CONCLUSIONS The establishment of off-label drug use evaluation system in cancer hospital is helpful to the rational use and refined management of clinical anti-tumor drugs.

To analyze the factors affecting the cost of hospitalization for patients and provide insights using the intrauterine lesion surgery group (DRG code NE19) as an example.

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.

ObjectiveTo construct a quantitative differentiation model of traditional Chinese medicine （TCM） syndromes by taking primary osteoporosis （POP） with kidney yang deficiency syndrome as an example， and to provide methodological reference for the standardization of syndrome differentiation. MethodsHigh-frequency clinical features of POP were screened by descriptive statistical analysis， and strong association features of POP were obtained by association rule algorithm. On this basis， a latent structure （latent tree） model was established through latent structure analysis， and the implicit and explicit variables （features） related to POP with kidney yang deficiency syndrome were comprehensively clustered， and the clustering results were interpreted by the indexes of mutual information and cumulative information coverage， to explore the primary and secondary symptoms， and to deduce the categories of POP with kidney yang deficiency syndrome based on the probability of the features appearing in the various latent categories. Based on the categories， the clinical feature scores and identification thresholds were calculated， and the syndrome differentiation model of POP with kidney yang deficiency was initially constructed by combining the comprehensive judgment rules. Finally， the results of TCM professionals' judgment were used as the gold standard to further evaluate the effectiveness of the model in assisting the syndrome differentiation. ResultsThe 32 features strongly associated with POP were obtained， and the Bayes information critedon score of the further constructed latent tree model was -15291.93. Based on the mutual information and the cumulative information coverage， the main symptoms of POP with kidney yang deficiency syndrome were bone weakness， fatigue， pale tongue， clear urine， frequent nocturnal urination， cold limbs， thin pulse， white coating， and secondary symptoms were weakness， loss of libido， loose stool， frequent urination， lumbar and knee weakness， and fear of cold. From the probability of the occurrence of each clinical feature in different latent categories of POP with kidney yang deficiency syndrome， the state was introduced as S0 category （none/mild kidney yang syndrome）/ S1 category （moderate kidney yang syndrome）/ S2 category （severe kidney yang syndrome）. Optimizing the preliminary rules of state identification and refining the state of S1 category， the results showed that among 970 patients with POP， there were 520 patients having no/mild kidney yang deficiency syndrome， 224 patients with moderate to mild kidney yang deficiency syndrome， 81 patients with moderate to severe kidney yang deficiency syndrome， and 145 patients with severe kidney yang deficiency syndrome. During the evaluation and validation process， the correct rate of the model assessment index was 0.8835， while the sensitivity was 0.7181， and the specificity was 0.9437. ConclusionCombined with the latent structure analysis of the association rule， the syndrome differentiation model for POP with kidney yang deficiency could be constructed， and the model shows a good quantitative identification effect， which can provide methodological supports for clinicians to improve the efficiency and accuracy of TCM diagnosis.

Objective:To explore the application value of virtual reality(VR) technology in the surgical diagnosis and treatment of congenital heart disease complicated with ventricular outflow tract stenosis.Methods:From November 2017 to October 2018, a total of 11 cases of congenital heart disease complicated with ventricular outflow tract stenosis were diagnosed and treated by VR technology assisted surgery in our center, including 9 cases of tetralogy of Fallot, 1 case of right ventricular double outlet stenosis and 1 case of right ventricular double outlet complicated with right ventricular outflow tract and pulmonary valve stenosis. The matching degree and value score of VR model by surgeons after operation. The data of these cases, including postoperative severe complications, maximum flow velocity and peak pressure difference and left ventricular ejection fraction(LVEF) 3 months after surgery, were retrospectively analyzed to evaluate the application value of VR technology and summarize the application experience of our center.Results:The operations were successful in all the 11 cases with no death in hospital. No serious complications related to the ventricular outflow tract occurred after the operation. The peak systolic velocity of the ventricular outflow tract in all the patients decreased to less than 2 m/s, and LVEF was in the normal range three months after the operation. In terms of VR model scores, the matching degree of all cases was 8/10 or above and 8 patients received a 3/3 of value score.Conclusion:For patients with congenital heart disease complicated with ventricular outflow tract stenosis, VR technology based on CT three-dimensional reconstruction can help surgeons more intuitively understand the spatial location information of each intra- and extra-cardiac structure and evaluate the feasibility of key surgical procedures, which is conducive to individual surgery and guarantees a good surgical outcome.

With potent biological activities, cellular growth factors are polypeptide factors that primarily stimulate cell growth and proliferation. They participate in the regulation of not only normal physiological functions such as human embryonic development and cell growth, but also neurorehabilitation and neuroplasticity in pathological processes such as nerve injury and recovery. Specifically, cellular growth factors have been shown to promote neuron survival, facilitate nerve regeneration and regulate synaptic plasticity, promote cell differentiation/vascular regeneration and modulate the microenvironment, promote nerve fiber myelination and improve nerve conduction. This review summarized current knowledge on the roles and various growth factors in neurorehabilitation and neuroplasticity, providing an update on potential clinical application of cellular growth factors in the field of neural rehabilitation.

There is a certain relationship between chemotherapy and stroke in cancer patients. Its mechanism may be associated with the increase of the prevalence of traditional vascular factors, the promotion of coagulation dysfunction, the induction of anemia, the impairment of cardiac function, and vascular inflammation. The pathophysiological mechanism of chemotherapy-associated stroke is still in the exploratory stage. This article reviews the pathophysiological mechanism, monitoring indicators, and diagnosis and treatment progress of stroke in cancer patients during chemotherapy.

Objective:To analyze the risk factors of intracranial hemorrhage after implanting 125-iodine seeds for brain tumors.Methods:A total of 234 patients with intracranial tumors receiving treatment of 125-iodine seeds from March, 2013 to November, 2020 were retrospectively analyzed. Patients were divided into bleeding group and non-bleeding group according to whether postoperative intracranial hemorrhage was reported. Univariate and multivariate analysis was performed by logistic regression to determine the independent risk factors of intracranial hemorrhage.Result:A total of 22 cases (9.4%) reported postoperative intracranial hemorrhage in 234 patients treated with 125-iodine seeds. Univariate analysis showed that the type of tumor and the history of anti-angiogenic drug within one month were possible risk factors ( P<0.1). Multivariate logistic regression analysis showed that anti-angiogenic drug within one month was the independent risk factor for intracranial hemorrhage ( P<0.05). Conclusions:The application of anti-angiogenic drugs within one month is the independent risk factor of intracranial hemorrhage with 125-iodine seeds for the treatment of brain tumors.