Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

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%.

Relation of COVID-19 with liver diseases and their impact on healthcare systems:The Portuguese case

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.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.

An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN

The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers.The goal is the remote monitoring of a patient’s physiological data by physicians.Moreover,this system can reduce the number and expenses of healthcare centers,make up for the shortage of healthcare centers in remote areas,enable consultation with expert physicians around the world,and increase the health awareness of communities.The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process,which should maintain the privacy of patients,and the integrity of remote medical instructions.Current research results indicate the need of a flexible authentication scheme.This study proposes a scheme with enhanced security for healthcare IoT systems,called an end-to-end authentication scheme for healthcare IoT systems,that is,an E2EA.The proposed scheme supports security services such as a strong and flexible authentication process,simultaneous anonymity of the patient and physician,and perfect forward secrecy services.A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks.A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication,computation,and storage,and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.

Organizational Contributions to Healthcare Worker (HCW) Burnout and Workplace Violence (WPV) Overlap: Is This an Opportunity to Sustain Prevention of Both?

The effects of Burnout in healthcare workers (HCW) are experienced by the worker, other staff, the institution and patients under their care on a daily basis. Workplace violence (WPV) has a spectrum of forms. In more extreme forms it generally is low frequency but has high impact when it occurs. Healthcare systems’ efforts to reduce Burnout are more likely to remain sustained since the impact is experienced daily and awareness is increasingly publicized. The efforts to reduce WPV are harder to sustain due to the lower frequency combined with daily competing administrative demands despite best intentions. Could efforts to reduce the overlapping organizational contributions to both HCW Burnout and WPV be a strategy to sustain prevention of WPV while preventing Burnout? A model of overlapping organizational contributions to HCW Burnout and WPV is built from supporting literature. Recommendations are made for leadership and management style interventions. Potential benefits would be higher quality and satisfaction in patient care by means of higher satisfaction in the delivery of care, recruitment and retention of excellent staff, retention of high quality institutional knowledge and reputation.

A Systematic Review on the Internet of Medical Things:Techniques,Open Issues,and Future Directions

IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems.

A service-based RBAC&MAC approach incorporated into the FHIR standard

Health Information Exchange(HIE)provides a more complete health record with the aim to improve patient care with relevant data gathered from multiple Health Information Technology(HIT)systems.In support of HIE,the Health Level Seven(HL7)XML standard was developed to manage,exchange,integrate,and retrieve electronic health information.In 2011,the Fast Healthcare Interoperable Resources(FHIR)standard,based on HL7,was proposed to facilitate the development of mobile Health(mHealth)apps with HIT data sharing via a common modeling format.FHIR utilizes RESTful APIs enabled with a FHIR server for information usage and exchange in the cloud.FHIR has a security specification,but does not define actual security mechanisms for secure data exchange via service invocations.If services are the primary means of access,there must be a way to control who can invoke which service at which time.This paper proposes the use of Role-Based Access Control(RBAC)and Mandatory Access Control(MAC)to define permissions based on role and/or the sensitivity level of services.This is accomplished by evolving RBAC and MAC to support permissions on services(as opposed to the usual object view)at a model level applied to a setting where a mobile application is using RESTful APIs.The resulting servicebased model is incorporated into the FHIR standard to control the access of who can invoke which services of FHIR RESTful APIs that manage the sensitive healthcare data;work is demonstrated via an mHealth application that interacts with the OpenEMR HIT system via the HAPI FHIR server.

Repairing the human with artificial intelligence in oncology

Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale.This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular.In this work,we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer,reinforcement or federated learning.Complementary,we also introduce the recently popular method of topological data analysis that improves the performance of learning models.

Analysis of the Desynchronization Attack Impact on the E2EA Scheme

The healthcare IoT system is considered to be a significant and modern medical system.There is broad consensus that these systems will play a vital role in the achievement of economic growth in numerous growth countries.Among the major challenges preventing the fast and widespread adoption of such systems is the failure to maintain the data privacy of patients and the integrity of remote clinical diagnostics.Recently,the author proposed an end-to-end authentication scheme for healthcare IoT systems(E2EA),to provide a mutual authentication with a high data rate between the communication nodes of the healthcare IoT systems.Although the E2EA authentication scheme supports numerous attractive security services to resist various types of attack,there is an ambiguous view of the impact of the desynchronization attack on the E2EA authentication scheme.In general,the performance of the authentication scheme is considered a critical issue when evaluating the applicability of such schemes,along with the security services that can be achieved.Therefore,this paper discusses how the E2EA authentication scheme can resist the desynchronization attack through all possible attack scenarios.Additionally,the effect of the desynchronization attack on the E2EA scheme performance is analyzed in terms of its computation and communication costs,based on a comparison with the recently related authentication schemes that can prevent such attack.Moreover,this research paper finds that the E2EA authentication scheme can not only prevent the desynchronization attack,but also offers a low cost in terms of computations and communications,and can maintain consistency and synchronization between the communication nodes of the healthcare IoT systems during the next authentication sessions.

Impact of hospital size on healthcare information system effectiveness: evidence from healthcare data analytics

With the rapid development of information technology, the increasing use ofmobile digital devices and efforts from the whole society, the healthcareinformation systems (HISs) are moving towards a new era. However, there isstill a lack of clear understanding of the benefits of HIS at the hospital leveland the influential factors for HIS effectiveness. In this study, we propose aresearch framework to explain how HIS implementation improves hospitalperformance. Our results reinforce the positive effect of HIS on hospitalperformance. In particular, we found that HIS implementation increases boththe cost and revenue of the hospitals, but the increasing effect in revenue ismuch bigger than the increasing effect in cost. We also found that althoughboth small and big hospitals benefit from the implementation of HIS, the effectof size is different. Size has a positive effect on hospital performance for smallhospitals but has a negative effect on big hospitals. This indicates that thecompetitive advantage of economies of scale disappears for big hospitalsbecause the level of information transparency becomes lower and transactioncosts become higher as size increases.

A queueing-inventory model to control the congestion of patients and medical waste in the medical centers, a case study

During epidemics,controlling the patients’congestion is a way to reduce disease spreading.Raising medical demands converts hospitals into one of the sources of disease outbreaks.The long patient waiting time in queues to receive medical services leads to more casualties.The rise of patients increases their waste,which is another source of disease outbreak.In this study,a mathematical model is developed to control patients’congestion in a medical center and manage their waste,considering environmental issues.Besides a queueing system controlling the patients’congestion in the treatment center,another queue is considered for vehicles.An inventory model is employed to prevent waste accumulation.The developed model is solved and reaches an exact solution in small size,and obtains an acceptable solution in large size using the Grasshopper algorithm.A case study is considered to demonstrate the model’s applicability.Also,Sensitivity analysis and valuable managerial insights are presented.

Current development in wearable glucose meters

Diabetes is one of the most disturbing chronic diseases in the world. The improvement of treatment efficiency brought by self-monitoring of blood glucose can relieve symptoms and reduce complications,which is considered as the gold standard of diabetes diagnosis and nursing. Compared to the traditional finger pricking measurement with painful and discontinuous processes, continuous blood glucose monitoring(CGM) presents superior advantages in wearable and continuous assessment of blood glucose levels. However, widely used implantable CGM systems at present require implantation operation and are highly invasive, so it is hard to be accepted by users. Except for the blood, available fluids in humans,such as interstitial fluid(ISF), sweat, tears and saliva, also contain glucose associated with blood sugar and can be extracted more easily. Therefore, these more accessible fluids are expected to realize minimized traumatic blood glucose monitoring. This review introduces the latest development of wearable minimally-/non-invasive CGM device, focusing on the types of blood substitute biological fluid and suitable monitoring approaches. We also analysis the merits and drawbacks of each method, and discuss the properties such as sensitivity, stability and convenience of each meter. Beyond highlighting recent key work in this field, we discuss the future development trend of wearable minimally-/non-invasive glucose meters.

Dynamic appointment scheduling for outpatient clinics with multiple physicians and patient choice

Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times.The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request.The patient may reject an undesirable appointment time slot with a certain probability,or may accept it,but the no-show probability will be higher.A stochastic overbooking model is formulated to maximize the expected profit,which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’overtime.A myopic scheduling policy is developed based on certain structural properties of the objective function.This study advances the study of appointment systems by generating a non-unimodal profit evolution.Moreover,both the decision of accommodating more requests for certain slots and the scheduling of appointments depend on the patient choice rather than the patient type.Further,computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.