Intelligent Medical System Research Articles

An Intelligent Medical Isolation Observation Management System Based on the Internet of Things.

Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff. We designed an intelligent crowd isolation medical observation management system framework based on the Internet of Things using wireless telemetry and big data cloud platform remote management technology. Through a smart wearable device with built-in sensors, vital sign data and geographical locations of medical observation subjects are collected and automatically uploaded to the big data monitoring platform on demand. According to the comprehensive analysis of the set threshold parameters, abnormal subjects are screened out, and activity tracking and health status monitoring for medical observation and management objectives are performed through monitoring and early warning management and post-event data traceability. In the trial of this system, the subjects wore the wristwatches designed in this study and real-time monitoring was conducted throughout the whole process. Additionally, for comparison, the traditional method was also used for these people. Medical staff came to measure their temperature twice a day. The subjects were 1,128 returned overseas Chinese from Europe. Compared with the traditional vital sign detection method, the system designed in this study has the advantages of a fast response, low error, stability, and good endurance. It can monitor the temperature, pulse, blood pressure, and heart rate of the monitored subject in real time. The system designed in this study and the traditional vital sign detection method were both used to monitor 1,128 close contacts with COVID-19. There were six cases of abnormal body temperature that were missed by traditional manual temperature measurement in the morning and evening, and these six cases (0.53%) were sent to the hospital for further diagnosis. The abnormal body temperature of these six cases was not found in time when the medical staff came to check the temperature on a twice-a-day basis. The system designed in this study, however, can detect the abnormal body temperature of all these six people. The sensitivity and specificity of our system were both 100%. The system designed in this study can monitor the body temperature, blood oxygen, blood pressure, heart rate, and geographical location of the monitoring subject in real time. It can be extended to COVID-19 medical observation isolation points, shelter hospitals, infectious disease wards, and nursing homes.

Strategic Modeling of Medical Intelligence as a Countermeasure for Future Pandemics

The coronavirus disease 2019 (COVID-19) evidently caused untold suffering and disruption of activities worldwide. Several hundreds of millions of individuals were infected, resulting in millions of deaths and socioeconomic losses in all regions of the world. It is possible that availability of information required to make a quick decision would have prevented the consequences of the disease, suggesting that the COVID-19 pandemic was an indication of a global medical intelligence failure. Indeed, medical intelligence is a critical aspect of intelligence gathering, responsible for collection, analysis, interpretation and characterization of the data to effectively monitor the outbreak of diseases and activate the required modalities and strategic planning to provide prompt preparedness and response countermeasures (including nonmedical and medical such as rehabilitation) to manage the public health risk or mitigate the incident. In this context, the role of technology, especially AI, ML and DL applications to support the medical intelligence is explored into this paper. The study followed systematic literature review to gather the information and arrive at strategic model. The study extends practical implications by suggesting the role of AI, ML and DL in medical intelligence, that should be considered as a fundamental arm of the national security architecture of a nation. With tremendous progress in technologies, further enhancement of medical intelligence can help to mitigate potential public health risks and future pandemics. There is an urgent demand for a better knowledge of what tasks information structures and technology scientists can play in this global pandemic related to medical intelligence. Current paper thus details the critical components of medical intelligence systems and networks required as countermeasures for future pandemics.

Observation of Curative Effect of Trimetazidine Combined with Metoprolol in Elderly Patients with Coronary Heart Disease Complicated with Heart Failure and the Effect of Myocardial Remodeling by Integrated Traditional Chinese and Western Medicine.

Diabetic retinopathy (DR) is the most common complication of diabetes and is often characterized by damage to retinal vascular microcirculation, resulting in retinal exudation, hemorrhage, fibrosis, and neovascularization. With the aging of my country's population, the incidence of DR is increasing year by year, and it has become one of the main blinding eye diseases in ophthalmic diseases also tends to be younger. So far, although the pathogenesis of DR is not completely clear, scholars generally believe that DR is based on the disorder of glucose metabolism, causing changes in the microcirculation of ocular tissues, nerves, and blood vessels, resulting in chronic damage to the nutrition and visual function of the eye disease. In order to explore the demand for cardiovascular disease treatment, make up for the lack of chronic diseases affecting people's physical harm, and improve the success rate of cardiovascular disease treatment, a method to observe the efficacy and myocardial remodeling of trimetazidine combined with metoprolol in elderly patients with coronary heart disease and heart failure based on integrated traditional Chinese and Western medicine was proposed. 54 elderly people over 60 years old are afraid of cardiovascular disease and take active protection. A method based on observation of integrated traditional Chinese and Western medicine was proposed, and at the same time, an intelligent medical monitoring system was constructed to better study, observe, and improve the efficacy of trimetazidine combined with metoprolol in elderly patients with coronary heart disease, heart failure, and myocardial impact of refactoring. The results of the study show that trimetazidine has a good clinical effect on ischemic cardiomyopathy heart failure based on the observation of integrated traditional Chinese and Western medicine.

Clinical Rehabilitation Nursing of Patients with Chronic Obstructive Pulmonary Disease Based on Intelligent Medicine.

Chronic obstructive pulmonary disease (COPD) is a respiratory disease that can be treated and prevented. The purpose of this paper is to conduct a meta-analysis of clinical rehabilitation nursing of COPD patients based on intelligent medical care by constructing a suitable model so as to make the research on clinical nursing of COPD patients more effective. This paper first introduced the intelligent medical system, analyzed the clinical rehabilitation nursing of COPD patients, established the SCNet model suitable for this paper, and then used statistical algorithms to carry out the meta dynamic analysis of the clinical rehabilitation nursing for COPD patients. Through the analysis of the current situation of medical equipment and the comparison of models and statistical analysis, the experimental results showed that the pulmonary function indexes of the pulmonary rehabilitation treatment group and the conventional treatment group were improved after treatment compared with before treatment. Although on specificity metrics SCNet did not perform the best, it was about 1% lower than the best baseline model. However, the comprehensive performance of the SCNet model on Acc, Sen, F1-Score, and AP indicators showed that the model SCNet proposed in this paper had certain advantages, which was helpful for the clinical rehabilitation care of patients with COPD and could better assist doctors in treatment.

Bibliometric analysis of research themes and trends in childhood autism spectrum disorders from 2012 to 2021.

BackgroundAutism spectrum disorders (ASD) are heterogeneous neurodevelopmental conditions that affect people worldwide. Early diagnosis and clinical support help achieve good outcomes. However, medical system structure and restricted resource availability create challenges that increase the risk of poor outcomes. Understanding the research progress of childhood ASD in recent years, based on clinical literature reports, can give relevant researchers and rehabilitation therapists more resonable research guides.ObjectiveThis bibliometric study aimed to summarize themes and trends in research on childhood ASD and to suggest directions for future enquiry.MethodsCitations were downloaded from the Web of Science Core Collection database on childhood ASD published from 1 January 2012, to 31 December 2021. The retrieved information was analyzed using CiteSpace.5.8. R3, and VOS viewer.ResultsA total of 7,611 papers were published across 103 areas. The United States was the leading source of publications. The clusters that have continued into 2020 include coronavirus disease 2019, gut microbiota, and physical activity, which represent key research topics. Keywords with frequency spikes during 2018–2021 were “disabilities monitoring network,” “United States,” and “caregiver.”ConclusionsThe Autism and Developmental Disabilities Monitoring Network in the United States can be used as a reference for relevant workers worldwide. An intelligent medical assistant system is being developed. Further studies are required to elucidate challenges associated with caring for a child with ASD.

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms.

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

Integrated Capacitive- and Resistive-Type Bimodal Relative Humidity Sensor Based on 5,10,15,20-Tetraphenylporphyrinatonickel(II) (TPPNi) and Zinc Oxide (ZnO) Nanocomposite.

The development of high-performance humidity sensors to cater for a plethora of applications, ranging from agriculture to intelligent medical monitoring systems, calls for the selection of a reliable and ultrasensitive sensing material. A simplistic device architecture, robust quantification of ambient relative humidity (% RH), and compatibility with the contemporary integrated circuit technology make a bimodal (capacitive and resistive) surface-type sensor to be a prominent choice for device fabrication. Herein, we have proposed and demonstrated a facile realization of a 5,10,15,20-tetraphenylporphyrinatonickel (II)–zinc oxide (TPPNi-ZnO) nanocomposite-based bimodal surface-type % RH sensor. The TPPNi macromolecule and ZnO nanoparticles have been synthesized by an eco-benign microwave-assisted technique and a thermal-budget chemical precipitation method, respectively. It is speculated from the morpohological study that specific surface area improvement, via the provision of ZnO nanoparticles on micro-pyramidal structures of TPPNi, may reinforce the sensing properties of the fabricated humidity sensor. The relative humidity sensing capacitive and resistive characteristics of the sensor have been monitored in 40–85% relative humidity (% RH) bandwidth. The fabricated sensor under the biasing conditions of 1 V of applied bias (Vrms) and 500 Hz AC test frequency exhibits a significantly higher sensitivity of 387.03 pF/% RH and 95.79 kΩ/% RH in bimodal operation. The average values of both the response and recovery times of the capacitive sensor have been estimated to be ∼30 s. It has also been debated why this high degree of sensitivity and considerable reduction in response/recovery time has been obtained. In addition, the intense and wide bandwidth spectral response of the TPPNi-ZnO nanocomposite indicates that it may also be utilized as a potential light-harvesting heterostructured nanohybrid in future studies.

Visual Attention and Motion Estimation-Based Video Retargeting for Medical Data Security

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

An Intelligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19.

The development of electronic services to help doctors in the diagnosis and treatment of COVID-19 is discussed. The existing systems for such purposes are analyzed, and requirements for them are formulated. The architecture of an intelligent medical decision support system and the basic principles of its development using an ontology-oriented approach are given. The unique capabilities of the system are shown, and its information and software components are described.

Medical Intelligent System and Orthopedic Clinical Nursing Based on Graph Partition Sampling Algorithm.

This study summarizes the process of graph partition sampling algorithm. Through studying, observing, and comparing many different types of graph partition sampling algorithms, this study represents the learning process sampling into three steps: sampling process, aggregation process, and the calculation process of loss function. The medical intelligence system developed in this study must provide remote audio and video connections between patients, nurses, and doctors, and requires hardware, software, and network collaboration. The hardware includes a server that keeps the medical intelligence system running. The software includes the operating system and server application software that provide the basic functions of the system. The network platform provides network bandwidth and basic configuration to achieve effective connections between different parts. At the same time, the system also defines audio and video encoding and decoding standards. Orthopedic patients are often accompanied by varying degrees of activity limitation and low self-care ability, the particularity of these patients puts forward higher requirements for orthopedic clinical care management. Orthopedic clinical nursing path is a standardized nursing management method supported by evidence-based medicine. In order to meet the requirements of the system design, the goal of the system design is to use the latest computing and network communication technology to organically “connect” the nursing staff, the nursing staff, and the hospital, so that the nursing staff can enjoy the personalized guidance specially made by the doctor at any time.

Case-Based Reasoning Diagnostic System for Antenatal Research Database

E-health is an exciting area of research with immense potential for benefit of mankind. The research in e-health domain is of such a nature that it provides an opportunity for researchers from different fields to work together for collective research benefit. Antenatal care is a critical issue for the health of the mother and new born. There are multiple complications which can arise during the pregnancy. The availability of all the relevant data for effective diagnosis and treatment is very important for every case of antenatal care. In this paper, we have proposed an efficient Intelligent Electronic Medical Record system for antenatal care and used Case-Based Reasoning to predict diagnosis of the patient in noisy environment. The experimental results obtained have confirmed the soundness of our approach. These results have also shown that performance of e-health can be enhanced when system development is a collective effort of various stakeholders including medical professionals, software developers and researchers.

Hindrance Identification of 5G Application in Healthcare: A Case Study of Jinan Residents

5G technology is an advanced technology based on cloud computing, mobile communication and Internet of Things, which has important advantages in the process of large-scale data transmission. 5G technology is of great significance to the development of the intelligent medical system with wise information technology (WIT120) as the core. At present, there are still various problems in technology, economic, institution, interest and ethics in the development of 5G healthcare. This study uses questionnaire interview and inductive analysis as the main research method, and takes Jinan residents as the interviewees, aiming to analyze and summarize the current situation of 5G technology application in healthcare, in order to identify the hindrance of 5G technology application and provide theoretical value for the popularization and development of 5G healthcare.

Monitoring and Analysis of Youth Sports Physique by Intelligent Medical Robot Based on Cognitive Computing.

As a key part of promoting the physical health of the people and promoting social progress, sports has ushered in comprehensive vitality. More and more people's attention has been paid to the physical health status, especially the physical status of young people. In the field of physical fitness monitoring, although the state has continuously increased investment in human and financial resources, it still cannot meet the needs of the market. This study mainly discusses the monitoring of youth sports physique by intelligent medical robots based on cognitive computing. This study introduces the development and implementation process of the management system and the test planning of the system. It focuses on the realization of the scalable architecture of the system server application and the communication mechanism of the intelligent terminal application. With the development of collaborative computing, social computing, and the ever-changing demands of human-computer interaction, it is difficult for a single user to take into account the interactive tasks in complex scenarios. The collaborative interaction of multiusers has gradually been paid more and more attention. Teenagers can log in to the intelligent medical robot system or mobile phone terminal to make health test appointments, score inquiries, and prescription inquiries. Its display module displays different contents according to different login identities. For teenagers, it is necessary to check personal physical test results and have a position on their physical health. Through the intelligent medical robot system, the administrator records and analyzes the results of the youth health test and gives appropriate exercise prescriptions for the youth. This not only analyzes the functional requirements that meet the basic user needs, but also analyzes the nonfunctional requirements that meet the most basic security, stability, and robustness of the software. Intelligent medical systems can effectively help people learn medical knowledge better in today's postepidemic era. It also helps people to conduct self-diagnosis and self-examination of minor diseases to a certain extent, so as to improve their own health. From the functional requirements, each module of the intelligent medical robot system is functionally described. From a nonfunctional point of view, the design and implementation meet the performance requirements of client-side robustness, maintainability, and stability. Finally, the system architecture of the intelligent medical robot is designed, and the specific database table is given. During the research, 20% of the students believed that their physical condition had been improved through physical health monitoring. The state of national physique can reflect a part of the country's comprehensive national strength to a certain extent. Throughout history, the overall development trend of a society and a country is constrained by the two factors of the group's constitution and economic development. This research will contribute to the physical and mental development of adolescents. The robot designed in this paper meets the requirements of a large number of students' centralized testing and is more accurate, faster, and more convenient.

Federated learning: Applications, challenges and future directions

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions.

Computer Internet of Things-Based Intelligent Medical System to Be Applied in Home Care of Senile Dementia Patients

It was to explore the accuracy of intelligent home medical system based on Internet of Things (IoT) technology and its application value in home care of senile dementia patients. Based on IoT, 5th generation wireless systems (5G), and smart wearable technology, intelligent home medical system was designed from the perspectives of system environment and system design. Smart wearable technology-based wearable smart clothes could recognize and collect electrocardiosignal, breathing signal, body sway signal, and body temperature signal. The data were transmitted to the application layer through the information transmission and processing modules of middle layer, and the extraction accuracy of behavioral features and behavior correct recognition rate of the system were analyzed. 64 senile dementia patients treated in the hospital between January 2019 and June 2020 were selected as the research objects. They were divided into control group (routine family care) and observation group (intelligent medical care system) according to different nursing methods. Each group included 32 cases. Activity of daily living (ADL) scores, nursing satisfaction, and the accidents during care of the patients in two groups before and after care were summarized. The results showed that the behavioral feature extraction accuracy of intelligent home medical system was above 74.59% and its correct recognition rate of different behaviors reached over 98.5%. ADL score in the observation group was lower than that in the control group 3 months after care ( P < 0.05 ). ADL score in the observation group was significantly lower than that in the control group ( P < 0.01 ) 6 months after care. The satisfaction of the observation group was 78.13% (25 cases), which was remarkably higher than that of the control group (31.25%, 10 cases) ( P < 0.001 ). The total satisfaction of the observation group amounted to 93.75% (30 cases), which was higher than that of the control group (68.75%, 22 cases) ( P < 0.01 ). The total incidence of accidents in the control and observation groups was 28.13% and 3.13%, respectively. Obviously, the total incidence of accidents in the observation group was higher than that in the control group ( P < 0.001 ). The above results showed that the established intelligent medical care system demonstrated potential application values in home care, which provided a new idea for the nursing methods for senile dementia patients.

Research on Urban Intelligent Medical Service System Design Based on Multiobjective Decision-Making Optimization Strategy

Aiming at the problems of difficult medical treatment for urban residents, low efficiency of hospitals, imbalance of medical resources and difficulty to meet the demands of patients, this paper proposes an urban intelligent medical service system based on multiobjective decision-making optimization strategy. Firstly, it analyzes the medical service demands of urban residents and points out that the urban intelligent medical service system should provide comprehensive services for residents; the main demands include basic medical demands, expected medical demands, and health care medical demands. Secondly, based on demand analysis, an urban intelligent medical service system composed of government, community hospital, large hospital, and urban residents is designed. The system has three kinds of system functions: customer business services, basic services, and institutional information services. Then, the overall process of the system before and after treatment of urban residents is introduced. Finally, according to the optimization objectives of the system: low medical cost, fast treatment speed, and high service satisfaction, the corresponding optimization strategies are formulated to optimize the urban intelligent medical system, reduce medical cost, improve treatment speed, and improve customer satisfaction, to better serve urban residents.

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.

Research on the Impact of Home Nursing Based on Intelligent Medical Internet of Things on the Quality of Life of Patients with Hemophilia.

The purpose of this paper is to study the feasibility and economic benefits of intelligent medical Internet of Things (IOT) systems for improving the quality of life of hemophilia patients, thereby reducing the risk of teratogenicity and disability for patients. This article selects 60 severe hemophilia patients who were followed up in our hospital from 2018 to 2019 as the research object. In the intelligent medical system, the Gaussian mixture model discretization algorithm is used to preprocess patient data collection. The observation group uses the intelligent medical system to implement home nursing for the patients, and the control group uses ordinary home nursing. This paper evaluates the quality of life of the two groups of patients 6 months after the intervention, including self-care ability, transfer function, and home nursing cognitive ability. The research results show that the home nursing based on smart medical IOT proposed in this paper is feasible and effective for improving the quality of life of patients. It can effectively improve the patient's self-care ability and joint functions and has important reference value for the development of intelligent medical IOT equipment.

A Big Data Analysis Algorithm Designed for the Interactive Platform of the Intelligent Sensor Information System

In order to improve the interactive sharing ability of the medical information system and improve the adaptability of the medical file access and management, a big data analysis algorithm designed for the intelligent sensor information system interaction platform is proposed. Using the information collection, statistics, and summary in the archives, adopt the statistical method analysis, optimize the medical archives intelligent sensor information adaptive scheduling and access algorithm, establish the medical information interactive sharing database, design the medical information system interactive sharing platform under the B/S structure system, query and update the database management of the sharing platform, and complete the software development of the medical archives intelligent sensor information embedded interactive sharing platform. Simulation results show the recall rate of 0.82 to 1, much higher than the other two comparison methods, with better data mining performance. Therefore, the intelligent sensor medical information system interaction platform based on big data analysis can improve the science and standardization of the medical business.

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.