Medical Smart Research Articles

A Visualized Dynamic Prediction Model for Lymphatic Metastasis in Ewing's Sarcoma for Smart Medical Services.

BackgroundThis study aims to predict the lymphatic metastasis in Ewing's sarcoma (ES) patients by nomogram. The risk of lymphatic metastasis in patients with ES was predicted by the built model, which provided guidance for the clinical diagnosis and treatment planning.MethodsA total of 929 patients diagnosed with ES were enrolled from the year of 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database. The nomogram was established to determine predictive factors of lymphatic metastasis according to univariate and multivariate logistic regression analysis. The validation of the model performed using multicenter data (n = 51). Receiver operating characteristics (ROC) curves and calibration plots were used to evaluate the prediction accuracy of the nomogram. Decision curve analysis (DCA) was implemented to illustrate the practicability of the nomogram clinical application. Based on the nomogram, we established a web calculator to visualize the risk of lymphatic metastases. We further plotted Kaplan-Meier overall survival (OS) curves to compare the survival time of patients with and without lymphatic metastasis.ResultsIn this study, the nomogram was established based on six significant factors (survival time, race, T stage, M stage, surgery, and lung metastasis), which were identified for lymphatic metastasis in ES patients. The model showed significant diagnostic accuracy with the value of the area under the curve (AUC) was 0.743 (95%CI: 0.714–0.771) for SEER internal validation and 0.763 (95%CI: 0.623–0.871) for multicenter data external validation. The calibration plot and DCA indicated that the model had vital clinical application value.ConclusionIn this study, we constructed and developed a nomogram with risk factors to predict lymphatic metastasis in ES patients and validated accuracy of itself. We found T stage (Tx OR = 2.540, 95%CI = 1.433–4.503, P < 0.01), M stage (M1, OR = 2.061, 95%CI = 1.189–3.573, P < 0.05) and survival time (OR = 0.982, 95%CI = 0.972–0.992, P < 0.001) were important independent factors for lymphatic metastasis in ES patients. Furthermore, survival time in patients with lymphatic metastasis or unclear situation (P < 0.0001) was significantly lower. It can help clinicians make better decisions to provide more accurate prognosis and treatment for ES patients.

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

Children are the reserve force for national construction, and children's health is now being paid more and more attention. In today's childhood asthma treatment, many are based on the treatment of common cough, and there is no strict division with asthma. Importantly, the efficacy of inhaled corticosteroids in the treatment of childhood asthma has been extensively studied in the medical community. However, there is no clear explanation for its specific efficacy and role. Research on childhood asthma has always been a key topic in medicine. This article aims to conduct a meta-analysis of the correlation between inhaled corticosteroids and asthma in children. This article proposes a more scientific literature screening strategy, which can achieve a higher accuracy rate for meta-analysis. It combines the intelligent medical health system in the analysis of children's asthma to assist in the analysis of curative effects. Such analysis also provides reference significance for related research. The experimental results indicate that inhaled corticosteroids have a good effect on childhood asthma. As the concentration of inhaled glucocorticoid increases, the effect will be better. Specifically, if the concentration is increased by 10%, the effect will be about 15% better, but there are corresponding side effects.

Active Management of Labor Process under Smart Medical Model Improves Vaginal Delivery Outcomes of Pregnant Women with Preeclampsia.

Background In a global environment of increasing cesarean delivery rate, promoting vaginal delivery, reducing the rate of first cesarean section, and the incidence of vaginal delivery complications are the objectives of obstetric medical quality and safety in China. As a common obstetric complication, preeclampsia affects the safety of many pregnant women. It is the obstetrician's great responsibility to promote vaginal delivery and improve delivery outcomes in preeclampsia. To this end, we explored the roles of active labor management under the smart medical model in improving the outcomes of vaginal delivery for pregnant women with preeclampsia. Methods The clinical data of 219 cases of preeclampsia pregnant women who delivered vaginally in our hospital from January 2017 to December 2020 were retrospectively analyzed. According to different labor process management, they were divided into study group (active labor process management group) and control group (normal labor process management group). Active labor process management methods included intrapartum ultrasound, central fetal heart rate monitoring, Doula delivery, labor analgesia, and quality of life care. The differences in delivery process, delivery outcome, bleeding causes, and hemostatic measures were compared between the two groups. Results (1) The incidence of preeclampsia in our hospital showed an increasing trend in recent four years; (2) in smart hospitals, the active management of labor process reduced the probability of transferring to the cesarean section in preeclampsia pregnant women with vaginal trial failure; and (3) active labor process management reduced the rate of lateral episiotomy, decreased the postpartum hemorrhage volume within two hours, and improved the vaginal delivery outcome of preeclampsia pregnant women. Conclusions In the era of the rapid development of the Internet, vigorously promoting the construction of smart hospitals and actively managing the delivery process can reduce the failure rate of vaginal trial delivery and improve the outcomes of vaginal delivery in preeclampsia women.

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

With the development of antibacterial, synergistic, and drug resistance research, the requirements for the specificity of antibiotics are getting higher and higher. In the research based on the specificity of antibiotics, this article uses intelligent medical image processing methods to study the specificity of the antibacterial efficiency of nanocopper oxide and the inhibition of drug resistance. Copper oxide nanorods have the properties of surface effect, quantum size effect, volume effect, and macroscopic quantum tunneling effect. Compared with ordinary copper oxide, the nanoscale gives them special properties of electricity, optics, and catalysis. In this article, in the research based on the specificity of antibiotics, the specificity of antimicrobial efficiency and drug resistance inhibition of nanocopper oxide are studied by using smart medical information processing methods. Drug sensitivity paper tablet method is a drug sensitivity experiment to determine drug sensitivity to make accurate and effective use of drugs for treatment. Colony growth method is used to take the equivalent volume of fermentation liquid at different times to determine the content of bacteria. In this article, Staphylococcus aureus is cultivated by the drug-sensitive disk method and the colony growth method. Then, according to this type of antibiotic and bacterial group combination, Staphylococcus aureus is divided into a penicillin group, nanocopper oxide group, and cephalosporin group. 0.5 g of the corresponding antibiotic was added to each group. TMP (trimethoprim) acts as a synergist, and the ratio of TMP to antibiotic is 1 : 5. Finally, we compared the inhibitory concentration indexes of the above three groups and inferred the synergistic effect of antibiotics and the inhibitory effect of drug resistance through the specificity of the antibiotics that the antibacterial activity was further studied. The results showed that the antibacterial effect of TMP combined with nano-CuO was 38% higher than that of the penicillin group and 41% higher than that of the cephalosporin group. In addition, the combined effect of TMP and antibiotics is greater than the combined effect of TMP and antibiotics alone. From the observation of smart medical system processing, it is speculated that the reason may be that they provide each other with a suitable environment. Because of this combined effect between the TMP and the antibiotic, it can influence each other. From the results, the combined effect is 48% higher than the combined effect. Therefore, according to the results of medical imaging, the combination of antibiotics and antibacterial synergists can improve specificity and antibacterial rate.

Diagnostic Imaging Analysis and Care of Patients with Endomyocardial Fibrosis Based on Wireless Network Smart Medical Application.

The heart is one of the most important organs of the human body, but in recent years heart disease has become one of the human health killers and this paper explores endomyocardial fibrosis, which is a common cardiomyopathy, commonly seen in infants and children, and refers to a diffuse elastic fibrous disease of the endocardium. The purpose of this paper is to explore the diagnostic imaging analysis and care of patients with endocardial heart machine fibrosis using wireless network intelligent medical technology, aiming to provide a new power basis for the treatment of the disease in related patients. This paper proposes a new endocardial segmentation algorithm that aims to process image information using image features, intervene in image noise reduction and smoothing, etc., and use image grayscale values to confirm cardiac cavity grayscale values as a basis for physicians to make certain judgments for the diagnosis of patients with endocardial machine fibrosis. The experimental results show that the atrial fibrillation group is distinctly higher compared to the sinus rhythm group, with values remaining between 25 and 39, which is a significant advantage compared to other methods.

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

Osteoporotic vertebral compression fractures are on the rise in modern society due to the aging population, and this often results in painful symptoms and kyphotic abnormalities in patients. Bone cement was injected into the vertebral body to reinforce the vertebral body and restore most of the damaged vertebrae's natural height. Percutaneous kyphoplasty is the name given to this type of procedure (PKP). Bone cement leakage has been linked to several problems, according to the research. Neurological problems might arise if bone cement leaks into the spinal canal or the nerve root canal during surgery. As a result, PKP surgeons must now deal with the issue of reducing bone cement leakage. Using smart medical big data, this paper examines a sample of PKP operations and then examines different associated aspects that influence complication rates in order to better advice clinical PKP surgery use. There were 172 vertebral bodies in total in 72 patients receiving PKP surgery at a Chinese hospital that were examined by smart medical big data for vertebral degeneration and fusion. Bone cement leakage and variations in preoperative average anterior vertebral column height, preoperative Cobb angle, and the volume of injected bone cement were considered when dividing the patients into leakage and nonleakage groups; then, we figure out what is causing the bone cement to leak. Five patients experienced lung-related problems out of the 18 vertebral bodies with bone cement leaking that were selected for study. That leakage rate was 10.5%. The differences between the two groups in terms of vertebral compression and bone cement injection were statistically significant based on a single-factor analysis. Bone cement leakage in PKP surgery has been linked to the amount of bone cement injected and whether the vertebral body's peripheral wall was injured, according to multivariate analysis. Lung-related problems are more common in patients with a prior history of illness. Osteoporotic vertebral compression fractures can be successfully treated with percutaneous kyphoplasty. An important risk factor for bone cement extravasation in PKP surgery is the amount of bone cement injected, as well as its viscosity and whether damage to the vertebral body's peripheral wall has occurred.

Analysis of the Smart Medical Service Model in Super-Aged Society-UR Agency as an Example.

Urban Renaissance (UR) Agency in Japan is one of the world's largest public housing institutions. When faced with the serious aging problems in Japan, the Japanese implemented innovative reform, with the smart medical and pension service launched according to the characteristics of resident population and their variation trends. As it well mastered the actual needs of residents by establishing the intelligent medical system, the occupancy rate was increased. Meanwhile, the problem of inadequate local medical resources was solved, with the satisfaction of residents as well as the cultural exchange and integration within communities ameliorated, which hence realized the sustainable development of communities. In this study, the smart medical experience of Urban Renaissance Agency in Japan was explored in the hope of providing enlightenment for the development of smart communities in China. Relevant Chinese enterprises can draw lessons from the experience of community services in Japan, and via the cooperation among industries, governments, and universities, they can collaborate with universities, scientific research institutions, high-tech enterprises, district governments, and grassroots communities to give full play to the advantages of the platform and improve service quality.

B-Ultrasound Image Analysis of Intrauterine Pregnancy Residues after Mid-Term Pregnancy Based on Smart Medical Big Data.

Abdominal B-ultrasound images of intrauterine pregnancy tissue residues were analyzed to discuss their diagnostic value. With the rapid development of computer technology and medical imaging technology, doctors are also faced with more and more medical image diagnosis tasks, and computer-aided diagnosis systems are especially important in order to reduce the work pressure of doctors. In recent years, deep learning has made rapid development and achieved great breakthroughs in various fields. In medical-aided diagnostic systems, deep learning has greatly improved the diagnostic efficiency, but there are no mature research results for abdominal B-ultrasound image recognition of intrauterine pregnancy tissue residues. Therefore, the study of liver ultrasound image classification based on deep learning has important practical application value. In this paper, we propose to give a CNN model optimization method based on grid search. Compared with the conventional CNN model design, this method saves time and effort by eliminating the need to manually adjust parameters based on experience and has an accuracy of more than 92% in classifying abdominal B-ultrasound images of intrauterine pregnancy tissue residues. The diagnosis of intrauterine pregnancy tissue residues by abdominal B-ultrasound can effectively improve the diagnosis and provide important reference for patients to receive treatment, which has high diagnostic value.

Surgical Strategy and Prognosis of Pancreatic Neuroendocrine Tumors Based on Smart Medical Imaging.

It is imperative to seize the “golden rescue time” and implement new concepts and new skills in modern trauma rescue. Combining with the development background of smart medical image analysis, this topic focuses on surgical strategies and prognostic measures and studies a serious and difficult disease frequently occurring in middle-aged and elderly people: pancreatic neuroendocrine tumors. This article uses the comparative test method and sample collection method to collect the medical records of patients with neuroendocrine tumors diagnosed by pathology from July 2010 to January 2018 in the First Affiliated Hospital of X City and analyze the samples with gender and age. At the same time, routine tumor marker examination and the location of NEN in the digestive system were performed. The distribution analysis of EUS characteristics of neuroendocrine tumor mucosa in each site was performed after operation, and the analysis of survival-related factors was performed during postoperative follow-up. The experimental data showed that among the tumor causes, WHO tumor grade (p < 0.05) and whether the surgical method was R0 resection (p < 0.05) were associated with prognosis. However, factors such as gender, age, and functional status were associated with prognosis. It has successfully completed the subject of surgical strategy and prognosis of pancreatic neuroendocrine tumors based on smart medical image analysis.

1-3 Piezoelectric Composite Ferroelectric Materials in Smart Medical Ultrasonic Diagnostic Transducers

1-3 Piezoelectric Composite Ferroelectric Materials in Smart Medical Ultrasonic Diagnostic Transducers

Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data.

The ultrasonic imaging research of nursing care for preventing and treating clinical infection of hemodialysis patients based on smart medical big data is studied. 100 hemodialysis patients were selected from May 2019 to May 2020. The patients were randomly divided into the observation group and routine group with 50 cases in each group. The PWV of common carotid artery was measured by ultrasonic rapid imaging technology, including BS value at the beginning of systole and ES value at the end of systole. According to the effect of preventive nursing intervention of intelligent medical treatment, the MHD group adopted preventive nursing intervention, while the routine group adopted traditional nursing service. The infection rate and quality of life score of patients in both groups were evaluated. The results showed that there were significant differences in BS and ES values between the MHD group and PWV in the normal group (P < 0.05). There was no significant difference in BS value and ES value between MHD patients with plaque and those without plaque (P > 0.05). It is proved that the ultrafast ultrasound imaging technology is safe, simple, noninvasive, nonradioactive, and fast and can automatically and accurately detect carotid PWV. It is expected to become a new imaging method for quantitative evaluation of arteriosclerosis degree in MHD patients. Preventive nursing intervention can reduce the incidence of infection in hemodialysis patients and improve their quality of life. Smart medical treatment has brought us a lot of convenience. As patients, we should change our concept, actively participate in it, and contribute to the development of smart medical treatment.

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare

Recently, the development and integration of various sensor control with smart intelligent unit is used in medical field through IoT. However, there is still a lot of space for growth in the medical and health industry's use of new technology. The traditional nurse care unit is managed through medical staffs, and the expanding medical demands creates the hospital’s patients records to be updated inefficiently. Since this is now an urgent need, developing a realistic, smart medical nursing care unit at low cost with a system capable of facilitating the effective and convenient administration of medical staff has taken a new significance. The proposed framework, conducted in the analysis to monitor medical records and activities of the emergency care unit patients, functions as a nurse and gives patients the nurse care satisfaction. The patients' actual location may be obtained for the first time by cloud computing based smart system. The precise location of the patient is critical to rescue the patient in emergency situation. This research work illustrates that the intelligent nurse care unit is the main phase called Smart Medical Nursing Care (SMNC). It contains several sensor units and by the combination of many sensors in the sensor module, it takes very less reaction time to connect or communicate both sides i.e., between patients and medical staffs.

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

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

Computer Management Design and Optimization of City Smart Medical Laboratory Service

In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.

Design of Smart Medical Mask Trash Can For Public Places

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;The Covid-19 pandemic mainly caused the sudden rise of medical mask waste. There are some steps that can be taken to reduce and manage the waste, including dissolving the mask so it cannot be use again, reduce the risk of disease transmission, and assist the workers in managing the waste of medical mask. Therefore, this paper proposes a device that can solve all the problem, which is the “Smart Medical Mask Trash Can”. This device can be used to dissolve and disinfect the mask, as well as manage the mask waste with an easy way in accordance with health protocol. The outcome show that this device can monitor the waste with an application program on a real time that can notify and organize a schedule for automatic disinfection. This tool is suitable to be placed in public places because of its high mobility and there is a lot of medical mask's waste. &lt;em&gt;&lt;/em&gt;&lt;/span&gt;&lt;/p&gt;

Analysis of the Effect of Emergency Ventilators on the Treatment of Critical Illness Based on Smart Medical Big Data.

Respiratory failure refers to pulmonary ventilation and ventilatory dysfunction caused by various reasons, which makes the patient unable to maintain the gas exchange required for stillness and causes a series of pathophysiological changes and corresponding clinical manifestations. In order to solve the problem of respiratory failure in critically ill patients, it is of great significance to analyze the role of microprocessor-based emergency ventilator in the treatment of critically ill patients. This article aims to study the role of microprocessor-based emergency ventilator in the treatment of critically ill patients. This paper presents the key technology based on the ARM11 processor. A breathing motion model is detected and established through a ventilator. The research objects are mainly divided into group A and group B. By comparing the two groups of emergency ventilator ventilation, it can effectively prevent the increase in respiratory muscle fatigue, reduce oxygen consumption, improve the patient's ventilation function and oxygen balance, quickly correct hypoxia and carbon dioxide storage, cooperate with drug treatment, and quickly take out the ventilator after relief. Good treatment results were achieved. The results show that the emergency ventilator controlled by a microcomputer is effective. The total effective rate of the control group was 71.11%, which was significantly lower than that of the observation group (86.67%).

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

At present, lung cancer ranks the first cause of tumor death in the world, and malignant tumors in the SPN detected by imaging account for 5–40%, most of which are peripheral lung cancer. The recovery of the solitary nodules in the lung after treatment has also been paid attention to. In order to explore the correlation of solitary pulmonary nodule (SPN) with microvessel density (MVD), vascular parameters, and vascular ratio under virtual reality images of smart medical treatment and evaluate the differentiation of SPN by ddPCR under virtual reality image observation diagnosis value, this article collects relevant information by investigating patients, investigating relevant literature, interviewing professionals, and constructing a case template, using a comprehensive quantitative and qualitative analysis method to create a damage assessment matrix. Experimental results prove that there are significant differences in the microvascular architecture within the SPN in the benign, inflammatory, and malignant groups. The correlation between ddPCR and vascular parameters (especially the ratio of luminal vessels) under the virtual reality image observation of smart medical treatment is better than other detection methods, and its accuracy is about 10% higher. Based on the observation of smart medical virtual reality images, ddPCR can be used as an index for noninvasive evaluation of tumor angiogenesis, which is helpful for the differential diagnosis of SPN.

Clinical Acupoint Selection for the Treatment of Functional Constipation by Massage and Acupuncture Based on Smart Medical Big Data Analysis.

With the development of society and economy, people's lifestyle and eating habits have undergone great changes, such as spending a long time behind desks, sitting for a long time, drinking and staying up late, and emotional depression; functional constipation, a disease of the digestive system, has changed. It is extremely common, and the age of onset is gradually decreasing. The development of the medical and health industry is also accompanied by the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, which penetrates into all aspects of the medical and health field and has entered the stage of smart medical care. This article proposes a study on the clinical acupoint selection rules of massage and acupuncture treatment of functional constipation based on smart medical big data analysis. This article adopts a variety of methods such as literature data method and experimental research method to carry out related theoretical research and promotion of massage and acupuncture treatment under the background of smart medical big data and design a clinical experiment of massage and acupuncture treatment based on big data analysis for functional constipation. The advantages of big data algorithms, the law of selecting acupoints in massage and acupuncture treatment, and the comparison of CCS symptom score and PAC-QOL score are analyzed. From the frequency of acupuncture treatment of functional constipation, the top 5 acupoints are Tianshu, Shangjuxu, Dachangshu, Zusanli, and Zhigou. In this paper, the total effective rate of treatment in the experimental group reached 96.56%, while the total effective rate of treatment in the control group was only 75.02%. Tuina and acupuncture treatment of functional constipation has a good therapeutic effect and is worthy of extensive clinical application.

Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications.

With the proliferation of IoMT (Internet of Medical Things), billions of connected medical devices are constantly producing oceans of time series sensor data, dubbed as time series for short. Considering these time series reflect various functional states of the human body, how to effectively detect the corresponding abnormalities is of great significance for smart healthcare. Accordingly, we develop a horizontal visibility graph-based temporal classification model for disease diagnosis. We conduct extensive comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. Besides, we have released the codes and parameters to facilitate the community research. We propose an identifiable temporal feature selection via horizontal visibility graph for time series classification (TSC) based disease diagnosis. We conduct comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. As a side contribution, we have released the codes and parameters to facilitate the community research ( https://github.com/sdujicun/SSVG ).

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

With the rapid development of society and the gradual improvement of people's living standards, patients with cardiovascular diseases have higher standards and requirements for daily health care and quality of life. The main research of this article is based on the observation of the effect of smart medical nursing intervention on the sleep quality of cardiology patients. The convenience sampling method was used to randomly select 80 cardiology patients from the electronic medical record system of the hospital, and the patients were randomly divided into two groups. One group was the control group. The routine nursing method was adopted, and a dedicated nurse paid attention to the sleep status; the other group was the control group. The research group adopts targeted nursing methods. This paper selects three kinds of sensor data as features. When collecting each scene record, it is first divided into sleeping and awake states, and then the classified time is composed of time segments, and these time segments are finally accumulated into sleep duration. Using sleep time, waking time, and sleep duration as input, the participants were divided into good sleepers and poor sleepers. Through a self-made questionnaire survey, the factors that have an adverse effect on the patient's sleep are divided into 7 aspects. Using the method of internal continuity measurement, Cronbach's coefficient a is 0.811, suggesting that the internal consistency is better, and the calculation reliability and validity are 86.1% and 83.4%, respectively, suggesting that the table can be used for sleep in hospitalized patients. The P values of the study group and the control group were 0.420, 0.764, 0.740, 0.881, 0.842, 0.119, and 0.342 (P > 0.05). The results show that the application of smart medical services has a certain effect on the patient's sleep quality.