Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and...Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.展开更多
Objective: Patient safety culture is a concern in every healthcare organization, therefore, the healthcare leadership is encountering issues related to patient safety across the globe. In India, there is limited resea...Objective: Patient safety culture is a concern in every healthcare organization, therefore, the healthcare leadership is encountering issues related to patient safety across the globe. In India, there is limited research and information about patient safety culture among healthcare stakeholders and there is relatively little qualitative research available that captures the factors of patient safety culture. Hence, this study aims to explore the perception of healthcare professionals on patient safety culture. Methods: An exploratory qualitative study design was adopted in a tertiary care hospital. Structured focus group discussion (FGD) (n = 4) among healthcare professionals and two in-depth interview focus groups were audio-recorded and transcribed. Two coders reviewed transcripts using the editing approach and organized codes into themes. The data were analyzed through MAXQDA 2022 (VERBI Software GmbH, Berlin, Germany), qualitative data analysis software, and descriptive analysis technique. The main codes and themes were generated using inductive and deductive method and smart coding was done. Results: Overall, there were 190 unique mentions of codes related to patient safety culture from 4 FGDs. They were categorized into 6 major themes and subcodes were derived via smart coding using the MAXQDA software. “Resources and constraints” was the most prominent code, followed by management support, manpower shortage, burnout, and lack of personnel commitment. Conclusions: The study highlights significant gaps in patient safety culture within the healthcare setting, with resource constraints, management support, and manpower shortages emerging as critical challenges. Burnout and lack of personnel commitment further exacerbate these issues, underscoring the need for targeted interventions.展开更多
With the rapid development of medical and nursing combinations,the application of humanistic care in medical and nursing combination institutions is getting more attention.Elderly institutions are the main carrier of ...With the rapid development of medical and nursing combinations,the application of humanistic care in medical and nursing combination institutions is getting more attention.Elderly institutions are the main carrier of elderly services in China,and the demand for humanistic care among the elderly in elderly institutions is also getting higher and higher,but at present,the humanistic care ability of the nursing staff in China's medical and nursing combined institutions is low.In recent years,the state vigorously promoted the development of traditional Chinese medicine,traditional Chinese medicine nursing contains a wealth of humanistic ideas,which can provide another solution for the lack of humanistic care in healthcare institutions.This paper discusses the ideological value,practical value and talent cultivation value of TCM humanistic nursing in medical care combination,aiming to provide a reference basis for improving the quality of humanistic nursing in medical care combination organizations.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
In the intricate landscape of healthcare,vicarious liability looms large,shaping the responsibilities and actions of healthcare practitioners and administrators alike.Illustrated by a poignant scenario of a medication...In the intricate landscape of healthcare,vicarious liability looms large,shaping the responsibilities and actions of healthcare practitioners and administrators alike.Illustrated by a poignant scenario of a medication error,this article navigates the complexities of vicarious liability in healthcare.It explains the legal basis and ramifications of this theory,emphasizing its importance in fostering responsibility,protecting patient welfare,and easing access to justice.The paper explores the practical effects of vicarious responsibility on day-to-day operations,leadership practices,and decision-making processes via the eyes of senior consultants,junior doctors,and hospital administrators.Through comprehensive insights and real-world examples,it underscores the imperative of fostering a culture of accountability,communication,and quality care to navigate the intricate web of liabilities inherent in modern healthcare.展开更多
BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confid...BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.展开更多
The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integrati...The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integration, quality, model interpretability, and clinical relevance. It suggests improvements in terms of hybrid machine learning models, enhanced methods for data preprocessing, and considerations on ethics. In such a way, it is trying to provide a roadmap for future research and practical implementation of predictive analytics in healthcare.展开更多
Background: Obesity is a chronic complex disease defined by excessive fat deposits that can impair health. Obesity occurs as a result of an imbalance in diet (energy intake) and physical activity (energy expended), mu...Background: Obesity is a chronic complex disease defined by excessive fat deposits that can impair health. Obesity occurs as a result of an imbalance in diet (energy intake) and physical activity (energy expended), multifactorial diseases due to obesogenic environment (availability of convenience food, media influence, etc.), psycho-social factors (social support systems, cultural/environmental influence, etc.) and genetic variants. Other causes are a subgroup of etiological factors (medications, diseases, immobilization, iatrogenic procedures, monogenic disease/genetic syndrome). Obesity is measured clinically by several common tools apart from body mass index (BMI), such as waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio, and neck circumference. WC and WHR are common tools for measuring central obesity while BMI measures generalized obesity. Aims: The goal of this study is to assess the prevalence of obesity amongst health workers of David Umahi Federal University Teaching Hospital, Uburu, Ebonyi state, Southeast Nigeria and to note the prevailing factors. A reliable estimate of the prevalence of obesity among health workers will contribute to the statistics needed to sway policymakers in the country to take urgent and substantial action on the increasing prevalence of obesity, especially in the healthcare industry. Methodology: The study was carried out between May 2024 and June 2024 at the David Umahi Federal University Teaching Hospital situated in Uburu, Ohaozara Local government area of Ebonyi state, Southeast Nigeria. The questionnaire was designed using the Finnish diabetic risk score (FINDRISC). It contained basic comprehending questions on age, gender, exposure to high blood pressure medication, and anthropometric measurement amongst others. Weight was taken with a portable weighing scale and height, with a stadiometer. Both were taken with shoes and headgear removed. The BMI was calculated using the weight (kg) divided by the square of the height (m2). Result: Generally, the prevalence of obesity (>30 kg/m2) in this study was low 17.6% (38), Overweight (BMI 25 - 30), 38.9%, (84) healthy Weight, (BMI 18.5 - 24.9), 43.5% (94). The study revealed that a family history of diabetes was significantly related to higher BMI, with participants more likely to be overweight or obese (p = 0.00030). Similarly, participants with a personal history of diabetes were predominantly in the obese category (p = 0.00038). Waist circumference also showed a strong association with BMI, as larger waist measurements were more common among obese individuals (p = 9.2 × 10−8). In contrast, the analysis found no significant relationships between BMI and age, gender, high blood pressure, or exercise habits. Conclusion: The socio-demographic determinants of obesity in this study were gender, age < 45 years and exposure to exercise. These determinants should form the areas of focus for interventions such as health education and the design of work environments as environments designed to promote physical activities while working will reduce the prevalence of obesity in tertiary institutions.展开更多
Artificial Intelligence(AI)has emerged as a transformative force in social welfare systems,providing innovative solutions to enhance efficiency,accessibility,and equity.This paper examines AI applications in social as...Artificial Intelligence(AI)has emerged as a transformative force in social welfare systems,providing innovative solutions to enhance efficiency,accessibility,and equity.This paper examines AI applications in social assistance,elderly care,and healthcare,demonstrating how predictive analytics,automation,and data-driven decision-making optimize service delivery.The research also explores the ethical,legal,and governance challenges of AI integration,including algorithmic bias,data privacy,and transparency.Furthermore,international policy comparisons illustrate diverse approaches to AI-driven welfare models.The study concludes with future research directions,emphasizing the need for ethical frameworks and regulatory oversight to ensure AI-driven social welfare remains inclusive and effective.展开更多
BACKGROUND Burnout syndrome is a significant issue among healthcare professionals worldwide,marked by depersonalization,emotional exhaustion,and a reduced sense of personal achievement.This psychological and physical ...BACKGROUND Burnout syndrome is a significant issue among healthcare professionals worldwide,marked by depersonalization,emotional exhaustion,and a reduced sense of personal achievement.This psychological and physical burden profoundly affects healthcare professionals'quality of care and overall well-being.In Somalia,where the healthcare system faces numerous challenges,the escalating demand for medical services and inadequate resources,coupled with overwhelming workloads,long hours,and high-stress levels,make healthcare providers particularly vulnerable to burnout syndrome.This,in turn,affects both the mental health of healthcare personnel and the quality of care they provide.AIM To examine the prevalence and determinants of burnout syndrome among healthcare practitioners in Mogadishu,Somalia.METHODS This cross-sectional prospective study was performed among 246 healthcare providers employed at a tertiary care hospital in Mogadishu,Somalia,who were recruited via random sampling.Data were collected using questionnaires that covered sociodemographic,psychological,work-related characteristics,and burnout syndrome.Bivariate and multivariate logistic regression analyses were performed to identify the variables that correlated with burnout syndrome.The results were presented using adjusted odds ratios(AORs),95%CIs,and P values,with a cutoff of 0.05 for identifying significant associations.RESULTS Among the participants,24%(95%CI:18.8%–29.8%)exhibited symptoms of burnout syndrome.Factors associated with burnout included female gender(AOR=6.60;95%CI:2.29-19.04),being married(AOR=3.07;95%CI:1.14-8.28),being divorced or widowed(AOR=5.84;95%CI:1.35-25.35),working more than 7 night shifts(AOR=3.19;95%CI:1.30–7.82),having less than 5 years of job experience(AOR=5.28;95%CI:1.29-21.65),experiencing poor sleep quality(AOR=5.29;95%CI:1.88-14.89),and exhibiting depressive(AOR=4.46;95%CI:1.59-12.53)and anxiety symptoms(AOR=7.34;95%CI:2.49-21.60).CONCLUSION This study found that nearly one in four healthcare professionals suffers from burnout syndrome.Improving sleep quality,monitoring,and providing mental health support could enhance their well-being and patient care.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
BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are es...BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are essential for improving patient outcomes and reducing morbidity and mortality associated with IBD.AIM To evaluate the knowledge,attitude and practice(KAP)of healthcare professionals regarding IBD.METHODS This cross-sectional study was conducted among healthcare professionals in China from November 2023 to December 2023 using a self-designed questionnaire.RESULTS A total of 315 valid questionnaires were analyzed,with 215 participants(68.25%)being female.The mean KAP scores were 17.55±5.35(range:0-24),27.65±2.77(range:8-40),and 18.88±4.23(range:6-30),respectively.Multivariate linear regression analysis revealed the following factors to be independently associated with knowledge:Age 26-35 years(β=2.80,95%CI:0.31-5.30,P=0.028),professional title(β=2.66,95%CI:0.91-4.41,P=0.003),position(β=-3.78,95%CI:-5.45 to-2.11,P<0.001),participation in IBD-related training(β=3.45,95%CI:2.39-4.51,P<0.001),and admission of more than five IBD cases in the past month(β=3.25,95%CI:1.58-4.92,P<0.001).Attitude was independently associated with knowledge(β=0.20,95%CI:0.15-0.26,P<0.001)and being a nurse or nursing supervisor(β=-1.30,95%CI:-2.16 to-0.40,P=0.003).Practice was independently associated with knowledge(β=0.20,95%CI:0.10-0.30,P<0.001)and attitude(β=0.24,95%CI:0.06-0.42,P=0.007).Structural equation modeling demonstrated direct effects of knowledge on attitude(β=0.24,P<0.001)and practice(β=0.26,P<0.001),as well as of attitude on practice(β=0.22,P=0.012).CONCLUSION Healthcare professionals demonstrated adequate knowledge but moderate attitude and inactive practice regarding IBD.Addressing the gaps in attitude and practice through targeted training programs and interventions is essential for improving patient care and outcomes.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
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
This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to en...This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.展开更多
Objective:Healthcare-seeking behavior(HSB)would affect the prevalence of morbidity and mortality.There are various factors that affect one's HSB.This study aimed to determine if health awareness and lifestyle migh...Objective:Healthcare-seeking behavior(HSB)would affect the prevalence of morbidity and mortality.There are various factors that affect one's HSB.This study aimed to determine if health awareness and lifestyle might relate to HSB.Methods:A cross-sectional study was applied by using three questionnaires to determine par ticipants'health awareness,lifestyle,and HSB.This study took place in Universitas Advent Indonesia and the students were recruited to be par ticipants.Results:There were 39 par ticipants joined in this study.Most of the par ticipants were females,third-year students,and from Accounting major.Almost all participants were aware of their low risk of health issues,had a fine lifestyle,and had moderate HSB.Conclusions:One's urge to seek health care facilities was not related to their health awareness and lifestyle.There was no fur ther study to contradict with this finding at this moment.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
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
As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in dat...As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.展开更多
This study aims to explore the challenges and opportunities associated with developing healthcare infrastructure in Saudi Arabia through the implementation of smart technologies. The healthcare sector in Saudi Arabia ...This study aims to explore the challenges and opportunities associated with developing healthcare infrastructure in Saudi Arabia through the implementation of smart technologies. The healthcare sector in Saudi Arabia is undergoing significant transformation, and the integration of smart technologies has the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance the overall healthcare experience. However, several challenges need to be addressed in order to fully leverage the benefits of smart technologies in healthcare infrastructure development. This research identifies and analyzes these challenges while also highlighting the opportunities that arise from the adoption of smart technologies in the Saudi Arabian healthcare system. The findings contribute to the understanding of the current state of healthcare infrastructure in Saudi Arabia and provide insights into the strategies and policies required to overcome challenges and maximize the benefits of smart technologies in healthcare.展开更多
文摘Efficient data management in healthcare is essential for providing timely and accurate patient care, yet traditional partitioning methods in relational databases often struggle with the high volume, heterogeneity, and regulatory complexity of healthcare data. This research introduces a tailored partitioning strategy leveraging the MD5 hashing algorithm to enhance data insertion, query performance, and load balancing in healthcare systems. By applying a consistent hash function to patient IDs, our approach achieves uniform distribution of records across partitions, optimizing retrieval paths and reducing access latency while ensuring data integrity and compliance. We evaluated the method through experiments focusing on partitioning efficiency, scalability, and fault tolerance. The partitioning efficiency analysis compared our MD5-based approach with standard round-robin methods, measuring insertion times, query latency, and data distribution balance. Scalability tests assessed system performance across increasing dataset sizes and varying partition counts, while fault tolerance experiments examined data integrity and retrieval performance under simulated partition failures. The experimental results demonstrate that the MD5-based partitioning strategy significantly reduces query retrieval times by optimizing data access patterns, achieving up to X% better performance compared to round-robin methods. It also scales effectively with larger datasets, maintaining low latency and ensuring robust resilience under failure scenarios. This novel approach offers a scalable, efficient, and fault-tolerant solution for healthcare systems, facilitating faster clinical decision-making and improved patient care in complex data environments.
文摘Objective: Patient safety culture is a concern in every healthcare organization, therefore, the healthcare leadership is encountering issues related to patient safety across the globe. In India, there is limited research and information about patient safety culture among healthcare stakeholders and there is relatively little qualitative research available that captures the factors of patient safety culture. Hence, this study aims to explore the perception of healthcare professionals on patient safety culture. Methods: An exploratory qualitative study design was adopted in a tertiary care hospital. Structured focus group discussion (FGD) (n = 4) among healthcare professionals and two in-depth interview focus groups were audio-recorded and transcribed. Two coders reviewed transcripts using the editing approach and organized codes into themes. The data were analyzed through MAXQDA 2022 (VERBI Software GmbH, Berlin, Germany), qualitative data analysis software, and descriptive analysis technique. The main codes and themes were generated using inductive and deductive method and smart coding was done. Results: Overall, there were 190 unique mentions of codes related to patient safety culture from 4 FGDs. They were categorized into 6 major themes and subcodes were derived via smart coding using the MAXQDA software. “Resources and constraints” was the most prominent code, followed by management support, manpower shortage, burnout, and lack of personnel commitment. Conclusions: The study highlights significant gaps in patient safety culture within the healthcare setting, with resource constraints, management support, and manpower shortages emerging as critical challenges. Burnout and lack of personnel commitment further exacerbate these issues, underscoring the need for targeted interventions.
文摘With the rapid development of medical and nursing combinations,the application of humanistic care in medical and nursing combination institutions is getting more attention.Elderly institutions are the main carrier of elderly services in China,and the demand for humanistic care among the elderly in elderly institutions is also getting higher and higher,but at present,the humanistic care ability of the nursing staff in China's medical and nursing combined institutions is low.In recent years,the state vigorously promoted the development of traditional Chinese medicine,traditional Chinese medicine nursing contains a wealth of humanistic ideas,which can provide another solution for the lack of humanistic care in healthcare institutions.This paper discusses the ideological value,practical value and talent cultivation value of TCM humanistic nursing in medical care combination,aiming to provide a reference basis for improving the quality of humanistic nursing in medical care combination organizations.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
文摘In the intricate landscape of healthcare,vicarious liability looms large,shaping the responsibilities and actions of healthcare practitioners and administrators alike.Illustrated by a poignant scenario of a medication error,this article navigates the complexities of vicarious liability in healthcare.It explains the legal basis and ramifications of this theory,emphasizing its importance in fostering responsibility,protecting patient welfare,and easing access to justice.The paper explores the practical effects of vicarious responsibility on day-to-day operations,leadership practices,and decision-making processes via the eyes of senior consultants,junior doctors,and hospital administrators.Through comprehensive insights and real-world examples,it underscores the imperative of fostering a culture of accountability,communication,and quality care to navigate the intricate web of liabilities inherent in modern healthcare.
文摘BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.
文摘The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integration, quality, model interpretability, and clinical relevance. It suggests improvements in terms of hybrid machine learning models, enhanced methods for data preprocessing, and considerations on ethics. In such a way, it is trying to provide a roadmap for future research and practical implementation of predictive analytics in healthcare.
文摘Background: Obesity is a chronic complex disease defined by excessive fat deposits that can impair health. Obesity occurs as a result of an imbalance in diet (energy intake) and physical activity (energy expended), multifactorial diseases due to obesogenic environment (availability of convenience food, media influence, etc.), psycho-social factors (social support systems, cultural/environmental influence, etc.) and genetic variants. Other causes are a subgroup of etiological factors (medications, diseases, immobilization, iatrogenic procedures, monogenic disease/genetic syndrome). Obesity is measured clinically by several common tools apart from body mass index (BMI), such as waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio, and neck circumference. WC and WHR are common tools for measuring central obesity while BMI measures generalized obesity. Aims: The goal of this study is to assess the prevalence of obesity amongst health workers of David Umahi Federal University Teaching Hospital, Uburu, Ebonyi state, Southeast Nigeria and to note the prevailing factors. A reliable estimate of the prevalence of obesity among health workers will contribute to the statistics needed to sway policymakers in the country to take urgent and substantial action on the increasing prevalence of obesity, especially in the healthcare industry. Methodology: The study was carried out between May 2024 and June 2024 at the David Umahi Federal University Teaching Hospital situated in Uburu, Ohaozara Local government area of Ebonyi state, Southeast Nigeria. The questionnaire was designed using the Finnish diabetic risk score (FINDRISC). It contained basic comprehending questions on age, gender, exposure to high blood pressure medication, and anthropometric measurement amongst others. Weight was taken with a portable weighing scale and height, with a stadiometer. Both were taken with shoes and headgear removed. The BMI was calculated using the weight (kg) divided by the square of the height (m2). Result: Generally, the prevalence of obesity (>30 kg/m2) in this study was low 17.6% (38), Overweight (BMI 25 - 30), 38.9%, (84) healthy Weight, (BMI 18.5 - 24.9), 43.5% (94). The study revealed that a family history of diabetes was significantly related to higher BMI, with participants more likely to be overweight or obese (p = 0.00030). Similarly, participants with a personal history of diabetes were predominantly in the obese category (p = 0.00038). Waist circumference also showed a strong association with BMI, as larger waist measurements were more common among obese individuals (p = 9.2 × 10−8). In contrast, the analysis found no significant relationships between BMI and age, gender, high blood pressure, or exercise habits. Conclusion: The socio-demographic determinants of obesity in this study were gender, age < 45 years and exposure to exercise. These determinants should form the areas of focus for interventions such as health education and the design of work environments as environments designed to promote physical activities while working will reduce the prevalence of obesity in tertiary institutions.
文摘Artificial Intelligence(AI)has emerged as a transformative force in social welfare systems,providing innovative solutions to enhance efficiency,accessibility,and equity.This paper examines AI applications in social assistance,elderly care,and healthcare,demonstrating how predictive analytics,automation,and data-driven decision-making optimize service delivery.The research also explores the ethical,legal,and governance challenges of AI integration,including algorithmic bias,data privacy,and transparency.Furthermore,international policy comparisons illustrate diverse approaches to AI-driven welfare models.The study concludes with future research directions,emphasizing the need for ethical frameworks and regulatory oversight to ensure AI-driven social welfare remains inclusive and effective.
文摘BACKGROUND Burnout syndrome is a significant issue among healthcare professionals worldwide,marked by depersonalization,emotional exhaustion,and a reduced sense of personal achievement.This psychological and physical burden profoundly affects healthcare professionals'quality of care and overall well-being.In Somalia,where the healthcare system faces numerous challenges,the escalating demand for medical services and inadequate resources,coupled with overwhelming workloads,long hours,and high-stress levels,make healthcare providers particularly vulnerable to burnout syndrome.This,in turn,affects both the mental health of healthcare personnel and the quality of care they provide.AIM To examine the prevalence and determinants of burnout syndrome among healthcare practitioners in Mogadishu,Somalia.METHODS This cross-sectional prospective study was performed among 246 healthcare providers employed at a tertiary care hospital in Mogadishu,Somalia,who were recruited via random sampling.Data were collected using questionnaires that covered sociodemographic,psychological,work-related characteristics,and burnout syndrome.Bivariate and multivariate logistic regression analyses were performed to identify the variables that correlated with burnout syndrome.The results were presented using adjusted odds ratios(AORs),95%CIs,and P values,with a cutoff of 0.05 for identifying significant associations.RESULTS Among the participants,24%(95%CI:18.8%–29.8%)exhibited symptoms of burnout syndrome.Factors associated with burnout included female gender(AOR=6.60;95%CI:2.29-19.04),being married(AOR=3.07;95%CI:1.14-8.28),being divorced or widowed(AOR=5.84;95%CI:1.35-25.35),working more than 7 night shifts(AOR=3.19;95%CI:1.30–7.82),having less than 5 years of job experience(AOR=5.28;95%CI:1.29-21.65),experiencing poor sleep quality(AOR=5.29;95%CI:1.88-14.89),and exhibiting depressive(AOR=4.46;95%CI:1.59-12.53)and anxiety symptoms(AOR=7.34;95%CI:2.49-21.60).CONCLUSION This study found that nearly one in four healthcare professionals suffers from burnout syndrome.Improving sleep quality,monitoring,and providing mental health support could enhance their well-being and patient care.
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.
文摘BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are essential for improving patient outcomes and reducing morbidity and mortality associated with IBD.AIM To evaluate the knowledge,attitude and practice(KAP)of healthcare professionals regarding IBD.METHODS This cross-sectional study was conducted among healthcare professionals in China from November 2023 to December 2023 using a self-designed questionnaire.RESULTS A total of 315 valid questionnaires were analyzed,with 215 participants(68.25%)being female.The mean KAP scores were 17.55±5.35(range:0-24),27.65±2.77(range:8-40),and 18.88±4.23(range:6-30),respectively.Multivariate linear regression analysis revealed the following factors to be independently associated with knowledge:Age 26-35 years(β=2.80,95%CI:0.31-5.30,P=0.028),professional title(β=2.66,95%CI:0.91-4.41,P=0.003),position(β=-3.78,95%CI:-5.45 to-2.11,P<0.001),participation in IBD-related training(β=3.45,95%CI:2.39-4.51,P<0.001),and admission of more than five IBD cases in the past month(β=3.25,95%CI:1.58-4.92,P<0.001).Attitude was independently associated with knowledge(β=0.20,95%CI:0.15-0.26,P<0.001)and being a nurse or nursing supervisor(β=-1.30,95%CI:-2.16 to-0.40,P=0.003).Practice was independently associated with knowledge(β=0.20,95%CI:0.10-0.30,P<0.001)and attitude(β=0.24,95%CI:0.06-0.42,P=0.007).Structural equation modeling demonstrated direct effects of knowledge on attitude(β=0.24,P<0.001)and practice(β=0.26,P<0.001),as well as of attitude on practice(β=0.22,P=0.012).CONCLUSION Healthcare professionals demonstrated adequate knowledge but moderate attitude and inactive practice regarding IBD.Addressing the gaps in attitude and practice through targeted training programs and interventions is essential for improving patient care and outcomes.
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.
文摘This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.
文摘Objective:Healthcare-seeking behavior(HSB)would affect the prevalence of morbidity and mortality.There are various factors that affect one's HSB.This study aimed to determine if health awareness and lifestyle might relate to HSB.Methods:A cross-sectional study was applied by using three questionnaires to determine par ticipants'health awareness,lifestyle,and HSB.This study took place in Universitas Advent Indonesia and the students were recruited to be par ticipants.Results:There were 39 par ticipants joined in this study.Most of the par ticipants were females,third-year students,and from Accounting major.Almost all participants were aware of their low risk of health issues,had a fine lifestyle,and had moderate HSB.Conclusions:One's urge to seek health care facilities was not related to their health awareness and lifestyle.There was no fur ther study to contradict with this finding at this moment.
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.
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.
基金We are thankful for the funding support fromthe Science and Technology Projects of the National Archives Administration of China(Grant Number 2022-R-031)the Fundamental Research Funds for the Central Universities,Central China Normal University(Grant Number CCNU24CG014).
文摘As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.
文摘This study aims to explore the challenges and opportunities associated with developing healthcare infrastructure in Saudi Arabia through the implementation of smart technologies. The healthcare sector in Saudi Arabia is undergoing significant transformation, and the integration of smart technologies has the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance the overall healthcare experience. However, several challenges need to be addressed in order to fully leverage the benefits of smart technologies in healthcare infrastructure development. This research identifies and analyzes these challenges while also highlighting the opportunities that arise from the adoption of smart technologies in the Saudi Arabian healthcare system. The findings contribute to the understanding of the current state of healthcare infrastructure in Saudi Arabia and provide insights into the strategies and policies required to overcome challenges and maximize the benefits of smart technologies in healthcare.
