Medical Smart Research Articles

Retracted: Application of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology.

[This retracts the article DOI: 10.1155/2021/5529798.].

Retracted: Improvement of Acupuncture Based on Smart Medical Care for Post-Stroke Hemiplegic Shoulder Pain and Upper Limb Motor Function.

[This retracts the article DOI: 10.1155/2021/9940645.].

Retracted: Value of ddPCR in the Preoperative Diagnosis of Solitary Pulmonary Nodules Based on the Observation of Virtual Reality Images of Smart Medical Treatment.

[This retracts the article DOI: 10.1155/2021/9920617.].

Retracted: Correlation Meta-Analysis of the Efficacy of Inhaled Corticosteroids in Children with Asthma Based on Smart Medical Health.

[This retracts the article DOI: 10.1155/2022/6220774.].

Retracted: Computer Management Design and Optimization of City Smart Medical Laboratory Service

Retracted: Computer Management Design and Optimization of City Smart Medical Laboratory Service

Retracted: Prevention of Venous Thrombosis of Lower Limbs after Cesarean Section Based on Smart Medical Air Pressure Therapy Instrument.

[This retracts the article DOI: 10.1155/2022/1451881.].

Retracted: Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning.

[This retracts the article DOI: 10.1155/2021/2474473.].

Retracted: Different Associated Aspects That Influence Complication Rates on Clinical PKP Surgery Using Smart Medical Big Data.

[This retracts the article DOI: 10.1155/2022/8432360.].

Retracted: Nursing Intervention Based on Smart Medical Care on the Sleep Quality of Cardiology Patients.

[This retracts the article DOI: 10.1155/2021/9947438.].

Retracted: Mechanism of Antibacterial Enhancement and Drug Resistance Based on Smart Medical Imaging on Antibiotics.

[This retracts the article DOI: 10.1155/2022/6103649.].

Retracted: Mitigating Psychological Trauma on Adult Burn Patients Based on Virtual Reality Technology of Smart Medical Treatment.

[This retracts the article DOI: 10.1155/2021/5531176.].

Retracted: Prevention and Nursing Research of PICC Catheter-Related Complications in Patients with Digestive System Malignant Tumor Based on Smart Medical Block Chain.

[This retracts the article DOI: 10.1155/2021/5519722.].

The Dissemination Strategy of an Urban Smart Medical Tourism Image by Big Data Analysis Technology.

The advanced level of medical care is closely related to the development and popularity of a city, and it will also drive the development of tourism. The smart urban medical system based on big data analysis technology can greatly facilitate people's lives and increase the flow of people in the city, which is of great significance to the city's tourism image dissemination and branding. The medical system, with eight layers of architecture including access, medical cloud service governance, the medical cloud service resource, the platform's public service, the platform's runtime service, infrastructure, and the overall security and monitoring system of the platform, is designed based on big data analysis technology. Chengdu city is taken as an example based on big data analysis technology to position the dissemination of an urban tourism image. Quantitative analysis and questionnaire methods are used to study the effect of urban smart medical system measurement and tourism image communication positioning based on big data analysis technology. The results show that the smart medical cloud service platform of the urban smart medical system, as a public information service system, supports users in obtaining medical services through various terminal devices without geographical restrictions. The smart medical cloud realizes service aggregation and data sharing compared to the traditional isolated medical service system. Cloud computing has been used as the technical basis, making the scalability and reliability of the system have unprecedented improvements. This paper discusses how to effectively absorb, understand, and use tools in the big data environment, extract information from data, find effective information, make image communication activities accurate, reduce the cost, and improve the efficiency of city image communication. The research shows that big data analysis technology improves patients' medical experience, improves medical efficiency, and alleviates urban medical resource allocation to a certain extent. This technology improves people's satisfaction with the dissemination of urban tourism images, makes urban tourism image dissemination activities accurate, reduces the cost of urban tourism image dissemination, and improves the efficiency of urban tourism image dissemination. The combination of the two can provide a reference for developing urban smart medical care and disseminating a tourism image.

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care.

Objective In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city. Methods A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared. Results After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant (P < 0.05). The scores of anxiety and depression in the group were lower, with statistical significance (P < 0.05); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant (P < 0.05); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant (P > 0.05). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant (P < 0.05). Conclusion The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women's self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care.

In order to alleviate the “difficulty in seeing a doctor” for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.

Prevention of Venous Thrombosis of Lower Limbs after Cesarean Section Based on Smart Medical Air Pressure Therapy Instrument.

Affected by the environment, the incidence of lower extremity venous thrombosis after cesarean section is gradually increasing, and postoperative nursing is becoming more and more important. The intelligent medical air pressure therapy instrument is a common and effective postpartum nursing method. This paper studies the role of pneumatic therapeutic apparatus in the prevention of lower extremity venous thrombosis after cesarean section in smart medicine, describes its importance in nursing, and analyzes the role of pneumatic therapeutic apparatus and smart medicine combined with the postoperative nursing situation of patients with cesarean section in our hospital. In this paper, we aggregate the description text of six levels of hospital intelligent service grading evaluation. In order to show the characteristics of each level and the requirements of each level of hospital intelligent service, we can roughly see the differences between different levels. Research shows that with the construction of medical information platform, more and more medical processes rely on hospital information system, which promotes the integration of medical platform and the interconnection of medical equipment, provides convenient services for patients, and provides patients with easier access to services. At the same time, the massive data generated by appointment, nursing, treatment, and other activities will be recorded, and the pneumatic therapeutic instrument can avoid the occurrence of 60% lower extremity venous thrombosis. The data of air pressure therapeutic instrument is dynamic, and its performance is mainly reflected in the recording of dynamic index data.

The Challenges of the Trend of Artificial Intelligence on the Construction of Civil Liability: Applied to Smart Medical Devices as an Example

在全球人口走向老化、且平均壽命不斷延長的趨勢下，對醫療照護的需求勢必有增無減。但與此同時，人口結構變遷的影響也使得醫療人力日漸吃緊。在此背景下，將現處於爆發性成長、又不會感到疲累的人工智慧引進醫療領域，由其來協助醫事人員完成任務，初步看來不論是對醫、病而言均具有相當實益。然而，醫療事務畢竟與人之生命、健康有密切關聯，稍有不慎即可能帶來嚴重後果。倘若醫師在診治病人的過程中使用了智慧醫療器材，但有誤判之狀況致病人受到生命、身體或健康上的損害，此時智慧醫療器材製造人或醫師有無可能須承擔相應的民事損害賠償責任？非無進一步探究之餘地，亦為本文關注之議題。Under the trend of an aging global population and a continual increase of average life expectancy, the need for medical care is bound to increase. However at the same time, the impact of demographic changes has also made medical workforce increasingly scarce. In this context, the introduction of artificial intelligence-which does not experience fatigue and is currently under explosive growth-to the medical field for assisting medical personnel to complete their tasks seems so far to be quite beneficial to both medical professionals and patients. However, after all, medical affairs are closely related to human life and health, and a little carelessness may result in serious consequences. If a doctor applies a smart medical device in the process of diagnosing and treating a patient, and a misdiagnosis occurs resulting in damage to the life, body or health of the patient, is it possible in this case that the smart medical device manufacturer or the doctor must bear the corresponding civil damages compensation? There is room for further research, and it is also the topic of concern in this paper.

Privacy Protection of Medical Service Data Based on Blockchain and Artificial Intelligence in the Era of Smart Medical Care

Smart medical care will realize the self-management, selection, and optimization of related things with more thorough induction, more comprehensive interconnection, and more intelligent insight, so that people can get an increasingly personalized medical and health service experience. 5G-enabled Internet of Things and AI (artificial intelligence) will continue to drive innovative applications in the medical industry. Access control and sharing of medical data is of great significance to the development of smart medical care, but the security problems in medical data sharing cannot be ignored. In this paper, a privacy protection scheme of medical service data based on blockchain and AI is proposed. The user chain is constructed as a public chain. In the user chain, the data privacy of users is protected, and users can safely transmit data to doctors and realize the management of session keys. CNN (convolutional neural network) privacy protection protocol based on homomorphic encryption can protect users’ privacy input, server model parameters, and calculated intermediate values. Experimental analysis and comparison with other schemes show that the scheme in this model is safer and more practical.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services.

BackgroundPancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.MethodsPatients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016–2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).ResultsAge, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715–0.735) and 0.711 (95%CI: 0.695–0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778–0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.ConclusionsA web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

Surgical Selection of T1 Stage Renal Tumor Resection Based on Imaging MAP Score under Smart Medical Care.

Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical staff and medical equipment. The combination of current technology and the medical field has become the norm. In the future, more artificial intelligence technologies will be integrated into the medical field to promote the development of medical care. At present, the information on the Internet is very large and complex, and general search engines often do not have knowledge in certain professional fields and can only perform shallow keyword searches. Therefore, it is difficult to meet people's medical diagnosis needs, and smart medical care can solve these needs. Medical imaging refers to the technology or process of obtaining internal tissue images of a certain part of the human body for medical research, including medical imaging systems and medical image processing. Medical image processing refers to the further processing of the obtained images, the purpose of which is either to restore the original image that was not clear enough or to highlight some characteristic information in the image. The purpose of this paper is to study the research on the selection of T1 stage renal tumor resection based on the imaging MAP score under smart medical care. It is hoped that through smart medicine and medical imaging technology, it can help renal tumor resection, reduce the sequelae of renal tumor resection, and promote the development of medical services. This paper proposes applying natural language processing technology to the medical field, creating an intelligent diagnosis assistance system, and using the existing medical record data to realize the corresponding medical assistance functions. It studies the class imbalance problem prevalent in medical datasets and provides better solutions through ensemble learning techniques to improve classifier performance when the number of positive and negative samples is unbalanced. The experimental results in this paper show that the creatinine of patients undergoing renal tumor resection combined with smart medicine and imaging technology is stable at 75 mol/L, while the creatinine is stable at 71 mol/L in other methods. It shows that the postoperative effect of smart medical treatment and imaging technology is better.