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
Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge...Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge of ensuring quality healthcare access. The overview of the effect of quality improvement initiatives in this paper focuses on private healthcare providers in Lagos State, Nigeria. The study assesses the impact of donor-funded quality improvement projects on these private healthcare facilities. It explores the level of participation, perceived support, and tangible effects of the initiatives on healthcare delivery within private healthcare facilities. It also examines how these initiatives influence patient inflow and facility ratings, and bring about additional benefits and improvements, provides insights into the challenges faced by private healthcare providers in implementing quality improvement projects and elicits recommendations for improving the effectiveness of such initiatives. Methods: Qualitative research design was employed for in-depth exploration, utilizing semi-structured interviews. Private healthcare providers in Lagos involved in the SP4FP Quality Improvement Project were purposively sampled for diversity. Face-to-face interviews elicited insights into participation, perceived support, and project effects. Questions covered participation levels, support perception, changes observed, challenges faced, and recommendations. Thematic analysis identified recurring themes from interview transcripts. Adherence to ethical guidelines ensured participant confidentiality and informed consent. Results: Respondents affirmed active involvement in the SP4FP Quality Improvement Project, echoing literature emphasizing private-sector collaboration with the public sector. While acknowledging positive influences on facility ratings, respondents highlighted challenges within the broader Nigerian healthcare landscape affecting patient numbers. Respondents cited tangible improvements, particularly in staff management and patient care processes, validating the positive influence of quality improvement projects. Financial constraints emerged as a significant challenge, aligning with existing literature emphasizing the pragmatic difficulties faced by private healthcare providers. Conclusions: This study illuminates the complex landscape of private healthcare provision in Lagos State, emphasizing the positive impact of donor-funded quality improvement projects. The findings provide nuanced insights, guiding policymakers, healthcare managers, and practitioners toward collaborative, sustainable improvements. As Nigeria progresses, these lessons will be crucial in shaping healthcare policies prioritizing population well-being.展开更多
Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human ...Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.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
The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are ...The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.展开更多
The medical metaverse and digital twin are set to revolutionise healthcare.Like all emerging technologies their benefits must be weighed against their ethical and social,impacts.If we consider the advances of medical ...The medical metaverse and digital twin are set to revolutionise healthcare.Like all emerging technologies their benefits must be weighed against their ethical and social,impacts.If we consider the advances of medical technology as an expression of our values,such as the pursuit of knowledge,cures and healing,an ethical study allows us to align our values and steer the technology towards an agreed goal.However,to appreciate the long-term consequents of a technology,those consequences must be considered in the context of a society already shaped by that technology.This paper identifies the technologies currently shaping society and considers the ethical,and social consequences of the medical metaverse and digital twin in that future society.展开更多
Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care w...Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care waste requires safe handling, treatment and disposal procedures. While incineration reduces the volume and quantity of waste for final disposal, it leads to the production of fly and bottom ashes laden with toxic incomplete combustion products such as Polycyclic Aromatic Hydrocarbons (PAHs), dioxins, furans and heavy metals. This exposes workers who handle and dispose the bottom ashes, hospital patients, the general public and environment. The goal of this study was to determine the total and individual levels of 16 most prevalent and toxic PAHs. Bottom ash samples were collected from incinerators in five county hospitals in Kenya, namely;Moi-Voi, Narok, Kitale, Makindu and Isiolo. Bottom ash samples were collected over a period of six months from the five hospitals. The samples were then sieved, homogenised and stored at 4°C in amber coloured glass containers. The PAHs were extracted using 30 ml of a hexane-acetone solvent (1:1) mixture by ultrasonication at room temperature (23°C) for 45 minutes. The PAHs were then analyzed with a GC-MS spectrophotometer model (Shimadzu GCMS-QP2010 SE) connected to a computer work station was used for the PAHs analysis. The GC-MS was equipped with an SGE BPX5 GC capillary column (30 m × 0.25 mm × 0.25 μm) for the separation of compounds. Helium was used as the carrier gas at a flow rate of 15.5 ml/minute and 14.5 psi. 1 μl of the sample was injected at 280°C, split mode (10:1). The oven programming was set for a total runtime of 40 minutes, which included: 100°C (2-minute hold);10°C /min rise to 200°C;7°C /min rise to 249°C;3°C /min rise to 300°C (2-minute hold). The interface temperature was set at 290°C. Analysis was done in Selected Ion Monitoring (SIM) mode and the peak areas of each of the PAHs were collected from the chromatograph and used for quantification of the 16 PAHs listed by the U.S. Environmental Protection Agency (EPA) which included, BaA (benz[a]anthracene: 4 rings), BaP (benzo[a]pyrene: 5 rings), BbF (benzo [b]fluoranthene: 5 rings), BkF (benzo[k]fluoranthene: 5 rings), Chr (chrysene: 4 rings), DbA (dibenz[a,h]anthracene: 5 rings), InP (indeno[1,2,3 - cd] pyrene: 6 rings) and Acp (acenaphthene: 3 rings), Acpy (acenaphthylene: 3 rings), Ant (anthracene: 3 rings), BghiP (benzo[g,h,i]perylene: 6 rings), Flu (fluorene: 3 rings), FluA (fluoranthene: 4 rings), Nap (naphthalene: 2 rings), PhA (phenanthrene: 3 rings) and Pyr (pyrene: 4 rings). Ion source-interface temperature was set at 200°C - 250°C. Internal standards from Sigma Aldrich were used in the analysis and the acquired mass spectra data were then matched against the NIST 2014 library [1] [2]. The mean PAHs concentration in the bottom ashes of each hospital varied broadly from 0.001 mg/kg to 0.4845 mg/kg, and the mean total concentration levels of individual PAHs ranged from 0.0072 mg/kg to 1.171 mg/kg. Low molecular weight PAHs (Phenanthrene, Naphthalene and Fluorene) were predominant in all the hospital wastes whereas Kitale and Narok presented the lowest PAHs concentrations and the lowest number of individual PAHs. Moi/Voi recorded the highest total PAHs concentration at 1.3129 ± 0.0023 mg/kg from a total of 11 PAHs being detected from the bottom ash samples. Narok had only three PAHs being detected at very low concentrations of 0.0041 ± 0.00 mg/kg, 0.0076 ± 0.00 mg/kg and 0.012 ± 0.00 mg/kg for phenanthrene, anthracene and chrysene respectively. This study presents hospital incinerator bottom ash as containing detectable levels of both carcinogenic and non-carcinogenic PAHs. Continued unprotected exposure of hospital workers (waste handlers) to the bottom ash PAHs could be hazardous to their health because of their cumulative effect. Preventive measures e.g. the use of Personal protective equipment (PPE) should be prioritised to minimise direct contact with the bottom ash. The study recommends an upgrade on incinerator technology for efficient combustion processes thus for better pollution control.展开更多
This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical educa...This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.展开更多
Background: Behind every great system is an organized team;this is especially true in the healthcare industry, where a dedicated human resources team can effectively recruit employees, train staff, and implement safet...Background: Behind every great system is an organized team;this is especially true in the healthcare industry, where a dedicated human resources team can effectively recruit employees, train staff, and implement safety measures in the workplace. The importance of human resources in the healthcare industry cannot be overstated, with benefits ranging from providing an orderly and effectively run facility to equipping staff with the most accurate and up-to-date training. Proper human resources management is critical in providing high-quality health care. A refocus on human resources management in healthcare requires more research to develop new policies. Effective human resources management strategies are greatly needed to achieve better outcomes and access to health care worldwide. Methods: This study leveraged NOI Polls census data on Health Facility Assessment for Lagos State. One thousand two hundred fifty-six health care facilities were assessed in Lagos State;numbers of Health workers were documented alongside their area of specialization. Also, demographic characterizations of the facilities, such as LGA, Ownership type, Facility Level Care, and Category of the facility, were also documented. Descriptive statistics alongside cross tabulation was done to present the various area of specialization of the health workers. Multiple response analysis was done to understand the distribution of human resources across the health facilities. At the same time, Chi-square and correlation tests were conducted to test the independence of various categories recorded while understanding the relationships among selected specialties. Results: The study revealed that Nurses were the most common health specialist in the Lagos State health facilities. At the same time, Gynecologists and General surgeons are the two medical specialists mostly common in health facilities. Midwives are the second most common health specialist working full time, while Generalist medical doctors make up the top three health specialists working full time. Nurses and Midwives had the highest number in Lagos State, while Pulmonologists were currently the lowest human resource available in Lagos State health care system. It was also noted that health facility distribution across Lagos’s urban and rural areas was even. In contrast, distribution based on other factors such as ownership type, Facility level of care, and facility category was slightly skewed. Conclusion: The distribution of health workers in health facility across LGA in Lagos State depend on Ownership type, Facility level of care, and category of the facility.展开更多
The outbreak of Covid-19 affects China’s health delivery system, and the current status of primary health services after the Covid-19 pandemic is not yet clear. To further explore the current status of demands of fam...The outbreak of Covid-19 affects China’s health delivery system, and the current status of primary health services after the Covid-19 pandemic is not yet clear. To further explore the current status of demands of family health services, we conducted a cross-sectional survey, in the community of Haidian District, Beijing. Chi-square test analysis and multivariate logistic regression models were used to identify factors influencing residents’ demands for family healthcare services. Results show that population of married (OR = 3.108), living with parents (OR = 2.171), degree of Junior high school and above (OR = 7.250) and high school (OR = 7.670), Annual income: 0 - 56,000 (OR = 3.680) and 72,001 - 88,000 (OR = 1.690) have significant demands for family health care. The approach to building primary health services in Haidian District is worth promoting, but it is also important to pay attention to the health inequalities that can occur when patients are moved down to the grassroots level. .展开更多
Diabetes is a non-communicable ailment that has adverse effects on the individual’s overall well-being and productivity in society.The main objective of this study was to examine the empirical literature concerning t...Diabetes is a non-communicable ailment that has adverse effects on the individual’s overall well-being and productivity in society.The main objective of this study was to examine the empirical literature concerning the association between diabetes and poverty and the accessibility and utilization of medical care services among diabetic patients.The diabetes literature was explored using a literature review approach.This review revealed that diabetes is an ailment that affects all individuals irrespective of socioeconomic status;however,its prevalence is high in low-income countries.Hence,despite the higher prevalence of diabetes in developing countries compared with developed countries,diabetes is not a poor man’s ailment because it affects individuals of all incomes.While the number of diabetic patients that access and utilize diabetes medical care services has increased over the years,some personal and institutional factors still limit patients’access to the use of diabetes care.Also,there is a lacuna in the diabetes literature concerning the extent of utilization of available healthcare services by diabetic patients.展开更多
The development of the Internet of Things has facilitated the rapid development of various industries.With the improvement in people’s living standards,people’s health requirements are steadily improving.However,owi...The development of the Internet of Things has facilitated the rapid development of various industries.With the improvement in people’s living standards,people’s health requirements are steadily improving.However,owing to the scarcity of medical and health care resources in some areas,the demand for remote surgery has gradually increased.In this paper,we investigate remote surgery in the healthcare environment.Surgeons can operate robotic arms to perform remote surgery for patients,which substantially facilitates successful surgeries and saves lives.Recently,Kamil et al.proposed a secure protocol for surgery in the healthcare environment.However,after cryptanalyzing their protocol,we deduced that their protocols are vulnerable to temporary value disclosure and insider attacks.Therefore,we design an improved authentication and key agreement protocol for remote surgeries in the healthcare environment.Accordingly,we adopt the real or random(ROR)model and an automatic verification tool Proverif to verify the security of our protocol.Via security analysis and performance comparison,it is confirmed that our protocol is a relatively secure protocol.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
Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to over...Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.展开更多
This study aims to assess and rank the service quality of the healthcare system utilizing a Fuzzy Analytical Hierarchical Process(Fuzzy AHP)andGreyRelationalAnalysis(Fuzzy GRA)technique.In this study,the six primary c...This study aims to assess and rank the service quality of the healthcare system utilizing a Fuzzy Analytical Hierarchical Process(Fuzzy AHP)andGreyRelationalAnalysis(Fuzzy GRA)technique.In this study,the six primary characteristics of healthcare service quality,comprising Tangibles(A),Healthcare Staff(B),Responsiveness(C),Relationships(D),Support Service(E),and Accessibility(F),are examined through a case study of 20 private and public hospitals in Hanoi,Vietnam.The weighting results of Fuzzy AHP technique indicated that Responsiveness(C)has the highest ranking,followed by Relationships(D)and Healthcare Staff(B).Meanwhile,Tangibility has finally comprised the next priority for increasing satisfaction with the service quality in the healthcare industry.Additionally,the highest service quality rankings are top 5 private hospitals via the Fuzzy GRA approach.Notably,this proposed approach may be applied to a complex decision-making process,which often makes sense with subjective data or imprecise information.展开更多
Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attract...Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.展开更多
Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both we...Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.展开更多
Objective:To identify the knowledge and awareness level of human mpox viral infection among healthcare workers in southwestern Nigeria.Methods:A cross-sectional study was conducted in Ekiti State,southwest Nigeria amo...Objective:To identify the knowledge and awareness level of human mpox viral infection among healthcare workers in southwestern Nigeria.Methods:A cross-sectional study was conducted in Ekiti State,southwest Nigeria among 316 healthcare workers that were selected through a systematic random sampling.Data were collected with the aid of a semi-structured,self-administered questionnaire.The Chi-square test and binary logistic regression were used to find the association between the independent and dependent variables.The significance level was set at P-value<0.05.Results:Two hundred and twenty-two(70.3%)of the respondents were aged≤40 years,mean age(36±9)years,189(59.8%)were female,306(96.8%)were Christians,and 203(64.2%)were married.Three hundred and fourteen(99.4%)of the respondents were aware of mpox infection.Main sources of information about mpox were medical education(44.0%),radio/television(32.0%)and newspaper(21.0%).However,among those aware of the disease,209(67.0%)demonstrated poor knowledge levels.Longer than 5 years’experience of medical practice was the only significant predictor of higher knowledge level of the disease(OR 1.76,95%CI 1.01-3.06;P=0.046).Conclusions:Despite the high awareness level of mpox infection among healthcare workers,there still exists a huge knowledge gap.It is recommended that targeted intervention could be directed towards continuous medical education and simulation exercises on re-emerging infectious diseases like mpox to improve the knowledge of the healthcare workers.展开更多
Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar...Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar City Administration public health institutions among 165 healthcare workers.Data were collected using a structured questionnaire.In addition,an on-spot observation checklist was used to assess the availability,status and management of the cold chain.A logistic regression model was used to assess the relationship between the outcome and predictor variables.Crude and adjusted odds ratios were calculated with 95%confidence intervals.Results:Overall,87(52.7%;95%CI 44.8%-60.5%)of the healthcare workers had unsatisfactory knowledge regarding the measles vaccine and its cold chain management.One hundred thirty-six(82.4%)healthcare workers correctly mentioned the recommended range of temperature(2-8℃)for measles vaccine storage.Healthcare workers aged 18-29 years(P=0.001)and 30-44 years(P=0.014)were observed as determinants of unsatisfactory knowledge on the measles vaccine and its cold chain management.One hundred and five(63.6%)of the healthcare workers did not correctly mention the type of measles vaccine used in routine immunization.More than one-third(36.4%)of the healthcare workers perceived that the measles vaccine is not safe and could cause measles.Conclusions:More than half of the healthcare workers in the study area had unsatisfactory knowledge on the measles vaccine and its cold chain management.It is necessary to provide technical support and in-service training for healthcare workers to ensure optimal immunization effectiveness.展开更多
The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorp...The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.展开更多
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Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge of ensuring quality healthcare access. The overview of the effect of quality improvement initiatives in this paper focuses on private healthcare providers in Lagos State, Nigeria. The study assesses the impact of donor-funded quality improvement projects on these private healthcare facilities. It explores the level of participation, perceived support, and tangible effects of the initiatives on healthcare delivery within private healthcare facilities. It also examines how these initiatives influence patient inflow and facility ratings, and bring about additional benefits and improvements, provides insights into the challenges faced by private healthcare providers in implementing quality improvement projects and elicits recommendations for improving the effectiveness of such initiatives. Methods: Qualitative research design was employed for in-depth exploration, utilizing semi-structured interviews. Private healthcare providers in Lagos involved in the SP4FP Quality Improvement Project were purposively sampled for diversity. Face-to-face interviews elicited insights into participation, perceived support, and project effects. Questions covered participation levels, support perception, changes observed, challenges faced, and recommendations. Thematic analysis identified recurring themes from interview transcripts. Adherence to ethical guidelines ensured participant confidentiality and informed consent. Results: Respondents affirmed active involvement in the SP4FP Quality Improvement Project, echoing literature emphasizing private-sector collaboration with the public sector. While acknowledging positive influences on facility ratings, respondents highlighted challenges within the broader Nigerian healthcare landscape affecting patient numbers. Respondents cited tangible improvements, particularly in staff management and patient care processes, validating the positive influence of quality improvement projects. Financial constraints emerged as a significant challenge, aligning with existing literature emphasizing the pragmatic difficulties faced by private healthcare providers. Conclusions: This study illuminates the complex landscape of private healthcare provision in Lagos State, emphasizing the positive impact of donor-funded quality improvement projects. The findings provide nuanced insights, guiding policymakers, healthcare managers, and practitioners toward collaborative, sustainable improvements. As Nigeria progresses, these lessons will be crucial in shaping healthcare policies prioritizing population well-being.
基金supported in part by the National Natural Science Foundation of China (NSFC) under Grant No.61976242in part by the Natural Science Fund of Hebei Province for Distinguished Young Scholars under Grant No.F2021202010+2 种基金in part by the Fundamental Scientific Research Funds for Interdisciplinary Team of Hebei University of Technology under Grant No.JBKYTD2002funded by Science and Technology Project of Hebei Education Department under Grant No.JZX2023007supported by 2022 Interdisciplinary Postgraduate Training Program of Hebei University of Technology under Grant No.HEBUT-YXKJC-2022122.
文摘Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.
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.
基金extend their appreciation to Researcher Supporting Project Number(RSPD2023R582)King Saud University,Riyadh,Saudi Arabia.
文摘The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.
文摘The medical metaverse and digital twin are set to revolutionise healthcare.Like all emerging technologies their benefits must be weighed against their ethical and social,impacts.If we consider the advances of medical technology as an expression of our values,such as the pursuit of knowledge,cures and healing,an ethical study allows us to align our values and steer the technology towards an agreed goal.However,to appreciate the long-term consequents of a technology,those consequences must be considered in the context of a society already shaped by that technology.This paper identifies the technologies currently shaping society and considers the ethical,and social consequences of the medical metaverse and digital twin in that future society.
文摘Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care waste requires safe handling, treatment and disposal procedures. While incineration reduces the volume and quantity of waste for final disposal, it leads to the production of fly and bottom ashes laden with toxic incomplete combustion products such as Polycyclic Aromatic Hydrocarbons (PAHs), dioxins, furans and heavy metals. This exposes workers who handle and dispose the bottom ashes, hospital patients, the general public and environment. The goal of this study was to determine the total and individual levels of 16 most prevalent and toxic PAHs. Bottom ash samples were collected from incinerators in five county hospitals in Kenya, namely;Moi-Voi, Narok, Kitale, Makindu and Isiolo. Bottom ash samples were collected over a period of six months from the five hospitals. The samples were then sieved, homogenised and stored at 4°C in amber coloured glass containers. The PAHs were extracted using 30 ml of a hexane-acetone solvent (1:1) mixture by ultrasonication at room temperature (23°C) for 45 minutes. The PAHs were then analyzed with a GC-MS spectrophotometer model (Shimadzu GCMS-QP2010 SE) connected to a computer work station was used for the PAHs analysis. The GC-MS was equipped with an SGE BPX5 GC capillary column (30 m × 0.25 mm × 0.25 μm) for the separation of compounds. Helium was used as the carrier gas at a flow rate of 15.5 ml/minute and 14.5 psi. 1 μl of the sample was injected at 280°C, split mode (10:1). The oven programming was set for a total runtime of 40 minutes, which included: 100°C (2-minute hold);10°C /min rise to 200°C;7°C /min rise to 249°C;3°C /min rise to 300°C (2-minute hold). The interface temperature was set at 290°C. Analysis was done in Selected Ion Monitoring (SIM) mode and the peak areas of each of the PAHs were collected from the chromatograph and used for quantification of the 16 PAHs listed by the U.S. Environmental Protection Agency (EPA) which included, BaA (benz[a]anthracene: 4 rings), BaP (benzo[a]pyrene: 5 rings), BbF (benzo [b]fluoranthene: 5 rings), BkF (benzo[k]fluoranthene: 5 rings), Chr (chrysene: 4 rings), DbA (dibenz[a,h]anthracene: 5 rings), InP (indeno[1,2,3 - cd] pyrene: 6 rings) and Acp (acenaphthene: 3 rings), Acpy (acenaphthylene: 3 rings), Ant (anthracene: 3 rings), BghiP (benzo[g,h,i]perylene: 6 rings), Flu (fluorene: 3 rings), FluA (fluoranthene: 4 rings), Nap (naphthalene: 2 rings), PhA (phenanthrene: 3 rings) and Pyr (pyrene: 4 rings). Ion source-interface temperature was set at 200°C - 250°C. Internal standards from Sigma Aldrich were used in the analysis and the acquired mass spectra data were then matched against the NIST 2014 library [1] [2]. The mean PAHs concentration in the bottom ashes of each hospital varied broadly from 0.001 mg/kg to 0.4845 mg/kg, and the mean total concentration levels of individual PAHs ranged from 0.0072 mg/kg to 1.171 mg/kg. Low molecular weight PAHs (Phenanthrene, Naphthalene and Fluorene) were predominant in all the hospital wastes whereas Kitale and Narok presented the lowest PAHs concentrations and the lowest number of individual PAHs. Moi/Voi recorded the highest total PAHs concentration at 1.3129 ± 0.0023 mg/kg from a total of 11 PAHs being detected from the bottom ash samples. Narok had only three PAHs being detected at very low concentrations of 0.0041 ± 0.00 mg/kg, 0.0076 ± 0.00 mg/kg and 0.012 ± 0.00 mg/kg for phenanthrene, anthracene and chrysene respectively. This study presents hospital incinerator bottom ash as containing detectable levels of both carcinogenic and non-carcinogenic PAHs. Continued unprotected exposure of hospital workers (waste handlers) to the bottom ash PAHs could be hazardous to their health because of their cumulative effect. Preventive measures e.g. the use of Personal protective equipment (PPE) should be prioritised to minimise direct contact with the bottom ash. The study recommends an upgrade on incinerator technology for efficient combustion processes thus for better pollution control.
文摘This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.
文摘Background: Behind every great system is an organized team;this is especially true in the healthcare industry, where a dedicated human resources team can effectively recruit employees, train staff, and implement safety measures in the workplace. The importance of human resources in the healthcare industry cannot be overstated, with benefits ranging from providing an orderly and effectively run facility to equipping staff with the most accurate and up-to-date training. Proper human resources management is critical in providing high-quality health care. A refocus on human resources management in healthcare requires more research to develop new policies. Effective human resources management strategies are greatly needed to achieve better outcomes and access to health care worldwide. Methods: This study leveraged NOI Polls census data on Health Facility Assessment for Lagos State. One thousand two hundred fifty-six health care facilities were assessed in Lagos State;numbers of Health workers were documented alongside their area of specialization. Also, demographic characterizations of the facilities, such as LGA, Ownership type, Facility Level Care, and Category of the facility, were also documented. Descriptive statistics alongside cross tabulation was done to present the various area of specialization of the health workers. Multiple response analysis was done to understand the distribution of human resources across the health facilities. At the same time, Chi-square and correlation tests were conducted to test the independence of various categories recorded while understanding the relationships among selected specialties. Results: The study revealed that Nurses were the most common health specialist in the Lagos State health facilities. At the same time, Gynecologists and General surgeons are the two medical specialists mostly common in health facilities. Midwives are the second most common health specialist working full time, while Generalist medical doctors make up the top three health specialists working full time. Nurses and Midwives had the highest number in Lagos State, while Pulmonologists were currently the lowest human resource available in Lagos State health care system. It was also noted that health facility distribution across Lagos’s urban and rural areas was even. In contrast, distribution based on other factors such as ownership type, Facility level of care, and facility category was slightly skewed. Conclusion: The distribution of health workers in health facility across LGA in Lagos State depend on Ownership type, Facility level of care, and category of the facility.
文摘The outbreak of Covid-19 affects China’s health delivery system, and the current status of primary health services after the Covid-19 pandemic is not yet clear. To further explore the current status of demands of family health services, we conducted a cross-sectional survey, in the community of Haidian District, Beijing. Chi-square test analysis and multivariate logistic regression models were used to identify factors influencing residents’ demands for family healthcare services. Results show that population of married (OR = 3.108), living with parents (OR = 2.171), degree of Junior high school and above (OR = 7.250) and high school (OR = 7.670), Annual income: 0 - 56,000 (OR = 3.680) and 72,001 - 88,000 (OR = 1.690) have significant demands for family health care. The approach to building primary health services in Haidian District is worth promoting, but it is also important to pay attention to the health inequalities that can occur when patients are moved down to the grassroots level. .
文摘Diabetes is a non-communicable ailment that has adverse effects on the individual’s overall well-being and productivity in society.The main objective of this study was to examine the empirical literature concerning the association between diabetes and poverty and the accessibility and utilization of medical care services among diabetic patients.The diabetes literature was explored using a literature review approach.This review revealed that diabetes is an ailment that affects all individuals irrespective of socioeconomic status;however,its prevalence is high in low-income countries.Hence,despite the higher prevalence of diabetes in developing countries compared with developed countries,diabetes is not a poor man’s ailment because it affects individuals of all incomes.While the number of diabetic patients that access and utilize diabetes medical care services has increased over the years,some personal and institutional factors still limit patients’access to the use of diabetes care.Also,there is a lacuna in the diabetes literature concerning the extent of utilization of available healthcare services by diabetic patients.
文摘The development of the Internet of Things has facilitated the rapid development of various industries.With the improvement in people’s living standards,people’s health requirements are steadily improving.However,owing to the scarcity of medical and health care resources in some areas,the demand for remote surgery has gradually increased.In this paper,we investigate remote surgery in the healthcare environment.Surgeons can operate robotic arms to perform remote surgery for patients,which substantially facilitates successful surgeries and saves lives.Recently,Kamil et al.proposed a secure protocol for surgery in the healthcare environment.However,after cryptanalyzing their protocol,we deduced that their protocols are vulnerable to temporary value disclosure and insider attacks.Therefore,we design an improved authentication and key agreement protocol for remote surgeries in the healthcare environment.Accordingly,we adopt the real or random(ROR)model and an automatic verification tool Proverif to verify the security of our protocol.Via security analysis and performance comparison,it is confirmed that our protocol is a relatively secure protocol.
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supported in part by a grant from the Victoria-Jiangsu Program for Technology and Innovation Research and Development。
文摘Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.
文摘This study aims to assess and rank the service quality of the healthcare system utilizing a Fuzzy Analytical Hierarchical Process(Fuzzy AHP)andGreyRelationalAnalysis(Fuzzy GRA)technique.In this study,the six primary characteristics of healthcare service quality,comprising Tangibles(A),Healthcare Staff(B),Responsiveness(C),Relationships(D),Support Service(E),and Accessibility(F),are examined through a case study of 20 private and public hospitals in Hanoi,Vietnam.The weighting results of Fuzzy AHP technique indicated that Responsiveness(C)has the highest ranking,followed by Relationships(D)and Healthcare Staff(B).Meanwhile,Tangibility has finally comprised the next priority for increasing satisfaction with the service quality in the healthcare industry.Additionally,the highest service quality rankings are top 5 private hospitals via the Fuzzy GRA approach.Notably,this proposed approach may be applied to a complex decision-making process,which often makes sense with subjective data or imprecise information.
基金financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior-Brazil(CAPES)-Finance Code 001the Spanish Ministry of Science and Innovation under grants PID2019-105381 GA-100(iScience).
文摘Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.
基金supported by the National Natural Science Foundation of China (62174115, U21A20147)the Natural Science Foundation of Jiangsu Province (BK20220284)+6 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (22KJB510013)the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924)the University Research Development Fund (RDF-17-01-13)the Key Program Special Fund in XJTLU (KSF-T-03, KSF-A-07)partially supported by the XJTLU AI University Research Centre and Jiangsu (Provincial) Data Science and Cognitive Computational Engineering Research Centre at XJTLUthe Collaborative Innovation Center of Suzhou Nano Science & Technologythe 111 Project and Joint International Research。
文摘Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.
文摘Objective:To identify the knowledge and awareness level of human mpox viral infection among healthcare workers in southwestern Nigeria.Methods:A cross-sectional study was conducted in Ekiti State,southwest Nigeria among 316 healthcare workers that were selected through a systematic random sampling.Data were collected with the aid of a semi-structured,self-administered questionnaire.The Chi-square test and binary logistic regression were used to find the association between the independent and dependent variables.The significance level was set at P-value<0.05.Results:Two hundred and twenty-two(70.3%)of the respondents were aged≤40 years,mean age(36±9)years,189(59.8%)were female,306(96.8%)were Christians,and 203(64.2%)were married.Three hundred and fourteen(99.4%)of the respondents were aware of mpox infection.Main sources of information about mpox were medical education(44.0%),radio/television(32.0%)and newspaper(21.0%).However,among those aware of the disease,209(67.0%)demonstrated poor knowledge levels.Longer than 5 years’experience of medical practice was the only significant predictor of higher knowledge level of the disease(OR 1.76,95%CI 1.01-3.06;P=0.046).Conclusions:Despite the high awareness level of mpox infection among healthcare workers,there still exists a huge knowledge gap.It is recommended that targeted intervention could be directed towards continuous medical education and simulation exercises on re-emerging infectious diseases like mpox to improve the knowledge of the healthcare workers.
文摘Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar City Administration public health institutions among 165 healthcare workers.Data were collected using a structured questionnaire.In addition,an on-spot observation checklist was used to assess the availability,status and management of the cold chain.A logistic regression model was used to assess the relationship between the outcome and predictor variables.Crude and adjusted odds ratios were calculated with 95%confidence intervals.Results:Overall,87(52.7%;95%CI 44.8%-60.5%)of the healthcare workers had unsatisfactory knowledge regarding the measles vaccine and its cold chain management.One hundred thirty-six(82.4%)healthcare workers correctly mentioned the recommended range of temperature(2-8℃)for measles vaccine storage.Healthcare workers aged 18-29 years(P=0.001)and 30-44 years(P=0.014)were observed as determinants of unsatisfactory knowledge on the measles vaccine and its cold chain management.One hundred and five(63.6%)of the healthcare workers did not correctly mention the type of measles vaccine used in routine immunization.More than one-third(36.4%)of the healthcare workers perceived that the measles vaccine is not safe and could cause measles.Conclusions:More than half of the healthcare workers in the study area had unsatisfactory knowledge on the measles vaccine and its cold chain management.It is necessary to provide technical support and in-service training for healthcare workers to ensure optimal immunization effectiveness.
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under grant no.KEP-4-120-42.
文摘The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.
