The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
BACKGROUND Globally,Liver cirrhosis is the 14th leading cause of death and poses a significant threat to human health.AIM To investigate the effects of a multidisciplinary collaboration model on postoperative recovery...BACKGROUND Globally,Liver cirrhosis is the 14th leading cause of death and poses a significant threat to human health.AIM To investigate the effects of a multidisciplinary collaboration model on postoperative recovery and psychological stress in patients with liver cirrhosis undergoing esophageal variceal bleeding(EVB)surgery within an integrated healthcare system.METHODS Between January 2022 and March 2024,a total of 180 patients with cirrhosis and EVB were admitted and randomly assigned to either a control group(standard care)or an observation group(standard care plus the multidisciplinary collaboration model),with 90 patients in each group.Postoperative recovery indicators(time to symptom improvement,time to start eating,time to bowel sound recovery,time to first flatus,and hospital stay),psychological stress responses[selfrating anxiety scale(SAS);self-rating depression scale(SDS)],subjective wellbeing,and incidence of complications were compared between the two groups.RESULTS Compared to the control group,the observation group showed earlier symptom improvement,earlier return to eating,bowel sound recovery,first flatus,and a shorter hospital stay.Pre-intervention SAS and SDS scores were not significantly different between the groups,but post-intervention scores were significantly lower in the observation group.Similarly,there was no significant difference in the subjective well-being scores before the intervention between the two groups.After the intervention,both groups showed improved scores,with the observation group scoring significantly higher than the control group.CONCLUSION The observation group also had a lower incidence of complications.Therefore,for patients with liver cirrhosis undergoing EVB surgery,a multidisciplinary collaboration model within an integrated healthcare system can promote early postoperative recovery,reduces psychological stress,improves subjective well-being,and reduces complications and rebleeding.展开更多
Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication direct...Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication directly affects patient outcomes in these settings;nonetheless,current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems.In dense installations where conventional approaches fail,this paper tackles the challenge of combining network efficiency with medical care priority.Thus,we offer a Dueling Deep Q-Network(DDQN)-based resource allocation approach for AI-native healthcare systems in 6G dense networks.First,we create a point-line graph coloringbased interference model to capture the unique characteristics of medical device communications.Building on this foundation,we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation.Unlike traditional graph-based models,this one correctly depicts the overlapping coverage areas common in hospital environments.Building on this basis,our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation.Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6%greater network throughput and 13.7%better resource use.The solution shows particularly strong in maintaining service quality under vital conditions with 5.5%greater Qo S satisfaction for emergency services and 8.2%quicker recovery from interruptions.展开更多
The healthcare sector involves many steps to ensure efficient care for patients,such as appointment scheduling,consultation plans,online follow-up,and more.However,existing healthcare mechanisms are unable to facilita...The healthcare sector involves many steps to ensure efficient care for patients,such as appointment scheduling,consultation plans,online follow-up,and more.However,existing healthcare mechanisms are unable to facilitate a large number of patients,as these systems are centralized and hence vulnerable to various issues,including single points of failure,performance bottlenecks,and substantial monetary costs.Furthermore,these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access.To address these issues,this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials.Furthermore,also utilize the capabilities of the InterPlanetary File System(IPFS)to store the Electronic Health Record(EHR)in a distributed way.This IPFS platform addresses not only the issue of high data storage costs on blockchain but also the issue of a single point of failure in the traditional centralized data storage model.The simulation results demonstrate that our model outperforms the benchmark schemes and provides an efficient mechanism for managing healthcare sector operations.The results show that it takes approximately 3.5 s for the smart contract to authenticate the node and provide it with the decryption key,which is ultimately used to access the data.The simulation results show that our proposed model outperforms existing solutions in terms of execution time and scalability.The execution time of our model smart contract is around 9000 transactions in just 6.5 s,while benchmark schemes require approximately 7 s for the same number of transactions.展开更多
With the increasing demands of health care,the design of hospital buildings has become increasingly demanding and complicated.However,the traditional layout design method for hospital is labor intensive,time consuming...With the increasing demands of health care,the design of hospital buildings has become increasingly demanding and complicated.However,the traditional layout design method for hospital is labor intensive,time consuming and prone to errors.With the development of artificial intelligence(AI),the intelligent design method has become possible and is considered to be suitable for the layout design of hospital buildings.Two intelli-gent design processes based on healthcare systematic layout planning(HSLP)and generative adversarial network(GAN)are proposed in this paper,which aim to solve the generation problem of the plane functional layout of the operating departments(ODs)of general hospitals.The first design method that is more like a mathemati-cal model with traditional optimization algorithm concerns the following two steps:developing the HSLP model based on the conventional systematic layout planning(SLP)theory,identifying the relationship and flows amongst various departments/units,and arriving at the preliminary plane layout design;establishing mathematical model to optimize the building layout by using the genetic algorithm(GA)to obtain the optimized scheme.The specific process of the second intelligent design based on more than 100 sets of collected OD drawings includes:labelling the corresponding functional layouts of each OD plan;building image-to-image translation with conditional ad-versarial network(pix2pix)for training OD plane layouts,which is one of the most representative GAN models.Finally,the functions and features of the results generated by the two methods are analyzed and compared from an architectural and algorithmic perspective.Comparison of the two design methods shows that the HSLP and GAN models can autonomously generate new OD plane functional layouts.The HSLP layouts have clear functional area adjacencies and optimization goals,but the layouts are relatively rigid and not specific enough.The GAN outputs are the most innovative layouts with strong applicability,but the dataset has strict constraints.The goal of this paper is to help release the heavy load of architects in the early design stage and present the effectiveness of these intelligent design methods in the field of medical architecture.展开更多
Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits.Because of the ethical and legal implications of a p...Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits.Because of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in healthcare.The image secrecy is highly vulnerable to various types of attacks.As a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection.To resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare networks.The blockchain algorithm preserves the security of the medical image by performing hash function.The execution of this research is carried out by MATLAB software.The suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound implications.During the simulation,94.9%of malicious communications were recognized and identified effectively,according to the total outcomes statistics.The suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorit...Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.展开更多
BACKGROUND The impact caused by the coronavirus disease 2019(COVID-19)on the Portuguese population has been addressed in areas such as clinical manifestations,frequent comorbidities,and alterations in consumption habi...BACKGROUND The impact caused by the coronavirus disease 2019(COVID-19)on the Portuguese population has been addressed in areas such as clinical manifestations,frequent comorbidities,and alterations in consumption habits.However,comorbidities like liver conditions and changes concerning the Portuguese population's access to healthcare-related services have received less attention.AIM To(1)Review the impact of COVID-19 on the healthcare system;(2)examine the relationship between liver diseases and COVID-19 in infected individuals;and(3)investigate the situation in the Portuguese population concerning these topics.METHODS For our purposes,we conducted a literature review using specific keywords.RESULTS COVID-19 is frequently associated with liver damage.However,liver injury in COVID-19 individuals is a multifactor-mediated effect.Therefore,it remains unclear whether changes in liver laboratory tests are associated with a worse prognosis in Portuguese individuals with COVID-19.CONCLUSION COVID-19 has impacted healthcare systems in Portugal and other countries;the combination of COVID-19 with liver injury is common.Previous liver damage may represent a risk factor that worsens the prognosis in individuals with COVID-19.展开更多
In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often gener...In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.展开更多
BACKGROUND This is a secondary database study using the Brazilian public healthcare system database.AIM To describe intestinal complications(ICs)of patients in the Brazilian public healthcare system with Crohn’s dise...BACKGROUND This is a secondary database study using the Brazilian public healthcare system database.AIM To describe intestinal complications(ICs)of patients in the Brazilian public healthcare system with Crohn’s disease(CD)who initiated and either only received conventional therapy(CVT)or also initiated anti-tumor necrosis factor(anti-TNF)therapy between 2011 and 2020.METHODS This study included patients with CD[international classification of diseases–10th revision(ICD-10):K50.0,K50.1,or K50.8](age:≥18 years)with at least one claim of CVT(sulfasalazine,azathioprine,mesalazine,or methotrexate).IC was defined as a CD-related hospitalization,pre-defined procedure codes(from rectum or intestinal surgery groups),and/or associated disease(pre-defined ICD-10 codes),and overall(one or more type of ICs).RESULTS In the 16809 patients with CD that met the inclusion criteria,the mean follow-up duration was 4.44(2.37)years.In total,14697 claims of ICs were found from 4633 patients.Over the 1-and 5-year of follow-up,8.3%and 8.2%of the patients with CD,respectively,presented at least one IC,of which fistula(31%)and fistulotomy(48%)were the most commonly reported.The overall incidence rate(95%CI)of ICs was 6.8(6.5–7.04)per 100 patient years for patients using only-CVT,and 9.2(8.8–9.6)for patients with evidence of anti-TNF therapy.CONCLUSION The outcomes highlighted an important and constant rate of ICs over time in all the CD populations assessed,especially in patients exposed to anti-TNF therapy.This outcome revealed insights into the real-world treatment and complications relevant to patients with CD and highlights that this disease remains a concern that may require additional treatment strategies in the Brazilian public healthcare system.展开更多
Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequ...Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.展开更多
With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves stor...With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.展开更多
In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contras...In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.展开更多
Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems....Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.展开更多
In many service delivery systems,the quantity of available resources is often a decisive factor of service quality.Resources can be personnel,offices,devices,supplies,and so on,depending on the nature of the services ...In many service delivery systems,the quantity of available resources is often a decisive factor of service quality.Resources can be personnel,offices,devices,supplies,and so on,depending on the nature of the services a system provides.Although service computing has been an active research topic for decades,general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen.Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century.Stochastic timed Petri nets(STPN),an extension to regular Petri nets,are a powerful tool for system performance evaluation.However,we did not find any single existing STPN software tool that supports all timed transition firing policies and server types,not to mention resource provisioning and requirement analysis.This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs.resource provisioning.The power of the simulation system is illustrated by an application to emergency health care systems.展开更多
The advancement of Unmanned Aerial Vehicle(UAV)technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil,business,and social applications.Gl...The advancement of Unmanned Aerial Vehicle(UAV)technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil,business,and social applications.Global rules in most countries had previously limited the use of drones to military applications due to their deployment in the open air,drones are likely to be lost,destroyed,or physically hijacked.However,more recently,the presence of COVID-19 has forced the world to present new implementing measures which will also widen the use of drones in civil and commercial and social applications,especially now in the delivery of medicines for medical home care.In the period of required public isolation as a consequence of the SARS-COV-2 pandemic,this knowledge has become one of the principal partners in the fight against the coronavirus.This paper offers a summary of the medical drone manufacturing,with a specific emphasis on its approval by the pharmaceutical sector to solve logistical problems in healthcare during times of sensitive need.We also discuss the numerous challenges to be met in the integration of drones to save our lives and suggest future research directions.The question that arises for this problem,how to optimize delivery medical supplies times in-home health care made up of drones?We conducted a synthesis literature review devoted to the use of UAVs in healthcare with their different aspects.A total of different research made are given to describe the role of UAV in Home healthcare with the presence of SARS-COV-2.We conclude that the drones will be able to optimize the way of eliminating contamination with a very high percentage(through the reduction of human contact)with the increase of the flexibility of the flight(reaching the less accessible regions every hour of the day).展开更多
The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during the...The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.展开更多
Background: Qatar, one of the smallest and wealthiest countries in the world, is a newly emerging healthcare system. Medical leadership in Qatar has had to create an infrastructure for medical care over the past twent...Background: Qatar, one of the smallest and wealthiest countries in the world, is a newly emerging healthcare system. Medical leadership in Qatar has had to create an infrastructure for medical care over the past twenty years. The purpose of this paper is to review the challenges and achievements of the newly emerging Qatar healthcare system. Methods: PubMed was searched using MESH terms: Qatar, healthcare, medical development, medical insurance and medical history. Websites of the World Bank, CIA fact book, Qatar Ministry of Health, Hamad Medical Corporation, Organization for Economic Co-operation and Development and the US State department were searched for information about Qatar’s healthcare system and its history. Results: Qatar is a rapidly growing, multicultural country with over 80 nationalities represented. Qatar has developed a healthcare system with universal coverage. Up until 2014, the government has subsidized all care. There are plans to develop a medical insurance system. Conclusions: Qatar has experienced the rapid development of a healthcare system over the past twenty years. The government has centrally controlled growth and development. An examination of the unique challenges to building a Qatari healthcare system will be useful in considering how to develop medical infrastructure in other countries.展开更多
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
文摘BACKGROUND Globally,Liver cirrhosis is the 14th leading cause of death and poses a significant threat to human health.AIM To investigate the effects of a multidisciplinary collaboration model on postoperative recovery and psychological stress in patients with liver cirrhosis undergoing esophageal variceal bleeding(EVB)surgery within an integrated healthcare system.METHODS Between January 2022 and March 2024,a total of 180 patients with cirrhosis and EVB were admitted and randomly assigned to either a control group(standard care)or an observation group(standard care plus the multidisciplinary collaboration model),with 90 patients in each group.Postoperative recovery indicators(time to symptom improvement,time to start eating,time to bowel sound recovery,time to first flatus,and hospital stay),psychological stress responses[selfrating anxiety scale(SAS);self-rating depression scale(SDS)],subjective wellbeing,and incidence of complications were compared between the two groups.RESULTS Compared to the control group,the observation group showed earlier symptom improvement,earlier return to eating,bowel sound recovery,first flatus,and a shorter hospital stay.Pre-intervention SAS and SDS scores were not significantly different between the groups,but post-intervention scores were significantly lower in the observation group.Similarly,there was no significant difference in the subjective well-being scores before the intervention between the two groups.After the intervention,both groups showed improved scores,with the observation group scoring significantly higher than the control group.CONCLUSION The observation group also had a lower incidence of complications.Therefore,for patients with liver cirrhosis undergoing EVB surgery,a multidisciplinary collaboration model within an integrated healthcare system can promote early postoperative recovery,reduces psychological stress,improves subjective well-being,and reduces complications and rebleeding.
基金supported by National Natural Science Foundation of China under Granted No.62202247。
文摘Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery,resource management in dense medical device networks stays a basic issue.Reliable communication directly affects patient outcomes in these settings;nonetheless,current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems.In dense installations where conventional approaches fail,this paper tackles the challenge of combining network efficiency with medical care priority.Thus,we offer a Dueling Deep Q-Network(DDQN)-based resource allocation approach for AI-native healthcare systems in 6G dense networks.First,we create a point-line graph coloringbased interference model to capture the unique characteristics of medical device communications.Building on this foundation,we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation.Unlike traditional graph-based models,this one correctly depicts the overlapping coverage areas common in hospital environments.Building on this basis,our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation.Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6%greater network throughput and 13.7%better resource use.The solution shows particularly strong in maintaining service quality under vital conditions with 5.5%greater Qo S satisfaction for emergency services and 8.2%quicker recovery from interruptions.
基金supported by the Ongoing Research Funding program(ORF-2025-636),King Saud University,Riyadh,Saudi Arabia.
文摘The healthcare sector involves many steps to ensure efficient care for patients,such as appointment scheduling,consultation plans,online follow-up,and more.However,existing healthcare mechanisms are unable to facilitate a large number of patients,as these systems are centralized and hence vulnerable to various issues,including single points of failure,performance bottlenecks,and substantial monetary costs.Furthermore,these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access.To address these issues,this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials.Furthermore,also utilize the capabilities of the InterPlanetary File System(IPFS)to store the Electronic Health Record(EHR)in a distributed way.This IPFS platform addresses not only the issue of high data storage costs on blockchain but also the issue of a single point of failure in the traditional centralized data storage model.The simulation results demonstrate that our model outperforms the benchmark schemes and provides an efficient mechanism for managing healthcare sector operations.The results show that it takes approximately 3.5 s for the smart contract to authenticate the node and provide it with the decryption key,which is ultimately used to access the data.The simulation results show that our proposed model outperforms existing solutions in terms of execution time and scalability.The execution time of our model smart contract is around 9000 transactions in just 6.5 s,while benchmark schemes require approximately 7 s for the same number of transactions.
基金the Scientific Research Project of Shanghai Science and Technology Commission(No.18DZ1205603)the Science Research Plan of Shanghai Municipal Science and Technology Committee(No.20DZ1201300)the National Key Research and Development Program of China(No.2017YFC0806100)。
文摘With the increasing demands of health care,the design of hospital buildings has become increasingly demanding and complicated.However,the traditional layout design method for hospital is labor intensive,time consuming and prone to errors.With the development of artificial intelligence(AI),the intelligent design method has become possible and is considered to be suitable for the layout design of hospital buildings.Two intelli-gent design processes based on healthcare systematic layout planning(HSLP)and generative adversarial network(GAN)are proposed in this paper,which aim to solve the generation problem of the plane functional layout of the operating departments(ODs)of general hospitals.The first design method that is more like a mathemati-cal model with traditional optimization algorithm concerns the following two steps:developing the HSLP model based on the conventional systematic layout planning(SLP)theory,identifying the relationship and flows amongst various departments/units,and arriving at the preliminary plane layout design;establishing mathematical model to optimize the building layout by using the genetic algorithm(GA)to obtain the optimized scheme.The specific process of the second intelligent design based on more than 100 sets of collected OD drawings includes:labelling the corresponding functional layouts of each OD plan;building image-to-image translation with conditional ad-versarial network(pix2pix)for training OD plane layouts,which is one of the most representative GAN models.Finally,the functions and features of the results generated by the two methods are analyzed and compared from an architectural and algorithmic perspective.Comparison of the two design methods shows that the HSLP and GAN models can autonomously generate new OD plane functional layouts.The HSLP layouts have clear functional area adjacencies and optimization goals,but the layouts are relatively rigid and not specific enough.The GAN outputs are the most innovative layouts with strong applicability,but the dataset has strict constraints.The goal of this paper is to help release the heavy load of architects in the early design stage and present the effectiveness of these intelligent design methods in the field of medical architecture.
文摘Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits.Because of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in healthcare.The image secrecy is highly vulnerable to various types of attacks.As a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection.To resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare networks.The blockchain algorithm preserves the security of the medical image by performing hash function.The execution of this research is carried out by MATLAB software.The suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound implications.During the simulation,94.9%of malicious communications were recognized and identified effectively,according to the total outcomes statistics.The suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
文摘Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.
文摘BACKGROUND The impact caused by the coronavirus disease 2019(COVID-19)on the Portuguese population has been addressed in areas such as clinical manifestations,frequent comorbidities,and alterations in consumption habits.However,comorbidities like liver conditions and changes concerning the Portuguese population's access to healthcare-related services have received less attention.AIM To(1)Review the impact of COVID-19 on the healthcare system;(2)examine the relationship between liver diseases and COVID-19 in infected individuals;and(3)investigate the situation in the Portuguese population concerning these topics.METHODS For our purposes,we conducted a literature review using specific keywords.RESULTS COVID-19 is frequently associated with liver damage.However,liver injury in COVID-19 individuals is a multifactor-mediated effect.Therefore,it remains unclear whether changes in liver laboratory tests are associated with a worse prognosis in Portuguese individuals with COVID-19.CONCLUSION COVID-19 has impacted healthcare systems in Portugal and other countries;the combination of COVID-19 with liver injury is common.Previous liver damage may represent a risk factor that worsens the prognosis in individuals with COVID-19.
基金This research work was funded by Institutional Fund Projects under grant no(IFPHI-050-611-2020)Therefore,authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University,Jeddah,Saudi Arabia.
文摘In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.
文摘BACKGROUND This is a secondary database study using the Brazilian public healthcare system database.AIM To describe intestinal complications(ICs)of patients in the Brazilian public healthcare system with Crohn’s disease(CD)who initiated and either only received conventional therapy(CVT)or also initiated anti-tumor necrosis factor(anti-TNF)therapy between 2011 and 2020.METHODS This study included patients with CD[international classification of diseases–10th revision(ICD-10):K50.0,K50.1,or K50.8](age:≥18 years)with at least one claim of CVT(sulfasalazine,azathioprine,mesalazine,or methotrexate).IC was defined as a CD-related hospitalization,pre-defined procedure codes(from rectum or intestinal surgery groups),and/or associated disease(pre-defined ICD-10 codes),and overall(one or more type of ICs).RESULTS In the 16809 patients with CD that met the inclusion criteria,the mean follow-up duration was 4.44(2.37)years.In total,14697 claims of ICs were found from 4633 patients.Over the 1-and 5-year of follow-up,8.3%and 8.2%of the patients with CD,respectively,presented at least one IC,of which fistula(31%)and fistulotomy(48%)were the most commonly reported.The overall incidence rate(95%CI)of ICs was 6.8(6.5–7.04)per 100 patient years for patients using only-CVT,and 9.2(8.8–9.6)for patients with evidence of anti-TNF therapy.CONCLUSION The outcomes highlighted an important and constant rate of ICs over time in all the CD populations assessed,especially in patients exposed to anti-TNF therapy.This outcome revealed insights into the real-world treatment and complications relevant to patients with CD and highlights that this disease remains a concern that may require additional treatment strategies in the Brazilian public healthcare system.
文摘Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.
文摘With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.
基金Supported by National Research Foundation of Korea Grant,No.00219725.
文摘In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.
文摘Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
文摘In many service delivery systems,the quantity of available resources is often a decisive factor of service quality.Resources can be personnel,offices,devices,supplies,and so on,depending on the nature of the services a system provides.Although service computing has been an active research topic for decades,general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen.Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century.Stochastic timed Petri nets(STPN),an extension to regular Petri nets,are a powerful tool for system performance evaluation.However,we did not find any single existing STPN software tool that supports all timed transition firing policies and server types,not to mention resource provisioning and requirement analysis.This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs.resource provisioning.The power of the simulation system is illustrated by an application to emergency health care systems.
文摘The advancement of Unmanned Aerial Vehicle(UAV)technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil,business,and social applications.Global rules in most countries had previously limited the use of drones to military applications due to their deployment in the open air,drones are likely to be lost,destroyed,or physically hijacked.However,more recently,the presence of COVID-19 has forced the world to present new implementing measures which will also widen the use of drones in civil and commercial and social applications,especially now in the delivery of medicines for medical home care.In the period of required public isolation as a consequence of the SARS-COV-2 pandemic,this knowledge has become one of the principal partners in the fight against the coronavirus.This paper offers a summary of the medical drone manufacturing,with a specific emphasis on its approval by the pharmaceutical sector to solve logistical problems in healthcare during times of sensitive need.We also discuss the numerous challenges to be met in the integration of drones to save our lives and suggest future research directions.The question that arises for this problem,how to optimize delivery medical supplies times in-home health care made up of drones?We conducted a synthesis literature review devoted to the use of UAVs in healthcare with their different aspects.A total of different research made are given to describe the role of UAV in Home healthcare with the presence of SARS-COV-2.We conclude that the drones will be able to optimize the way of eliminating contamination with a very high percentage(through the reduction of human contact)with the increase of the flexibility of the flight(reaching the less accessible regions every hour of the day).
文摘The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.
文摘Background: Qatar, one of the smallest and wealthiest countries in the world, is a newly emerging healthcare system. Medical leadership in Qatar has had to create an infrastructure for medical care over the past twenty years. The purpose of this paper is to review the challenges and achievements of the newly emerging Qatar healthcare system. Methods: PubMed was searched using MESH terms: Qatar, healthcare, medical development, medical insurance and medical history. Websites of the World Bank, CIA fact book, Qatar Ministry of Health, Hamad Medical Corporation, Organization for Economic Co-operation and Development and the US State department were searched for information about Qatar’s healthcare system and its history. Results: Qatar is a rapidly growing, multicultural country with over 80 nationalities represented. Qatar has developed a healthcare system with universal coverage. Up until 2014, the government has subsidized all care. There are plans to develop a medical insurance system. Conclusions: Qatar has experienced the rapid development of a healthcare system over the past twenty years. The government has centrally controlled growth and development. An examination of the unique challenges to building a Qatari healthcare system will be useful in considering how to develop medical infrastructure in other countries.
