Showing papers in "Health technology in 2021"

Application of telemedicine and eHealth technology for clinical services in response to COVID­19 pandemic.

Abstract: Telemedicine and eHealth refer to the use of information and communication technology (ICT) embedded in software programs with highspeed telecommunications systems for delivery, management, and monitoring of healthcare services. Application of telemedicine have become timely while providing great potentials to protect both medical practitioners and patients, as well as limit social mobility of patients contributing to reduce the spread of the virus. This study employs data from the existing literature to describe the application of telemedicine and eHealth as a proactive measure to improve clinical care. Findings from this study present the significance of telemedicine and current applications adopted during the pandemic. More importantly, the findings present practical application of telemedicine and eHealth for clinical services. Also, polices initiated across the world to promote management of COVID-19 are discussed. Respectively, this study suggests that telemedicine and eHealth can be adopted in times of health emergency, as a convenient, safe, scalable, effective, and green method of providing clinical care.

Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis

Abstract: Nowadays, people are getting caught in their day-to-day lives doing their work and other things and ignoring their health. Due to this hectic life and ignorance towards their health, the number of people getting sick increases every day. Moreover, most of the people are suffering from a disease like heart disease. Global deaths of almost 31% population are due to heart-related disease as data contributed by the World Health Organization (WHO). So, the prediction of happening heart disease or not becomes important for the medical field. However, data received by the medical sector or hospitals is so huge that sometimes it becomes difficult to analyze. Using machine learning techniques for this prediction and handling of data can become very efficient for medical people. Hence in this study, we have discussed the heart disease and its risk factors and explained machine learning techniques. Using that machine learning techniques, we have predicted heart disease and provided a comparative analysis of the algorithms for machine learning used for the experiment of the prediction. The goal or objective of this research is completely related to the prediction of heart disease via a machine learning technique and analysis of them.

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Abstract: The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

Abstract: The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Impact of the recreational use of virtual reality on physical and mental wellbeing during the Covid-19 lockdown.

Abstract: The Covid-19 pandemic has brought about significant changes to most aspects of our lives. As a result of the quarantine enforced by governments and authorities worldwide, people had to suddenly adapt their daily routines, including work, study, diet, leisure and fitness activities to the new circumstances. A growing body of research indicates that the engagement with virtual reality (VR) activities can have a positive impact on users' mental and physical wellbeing. This study aims to evaluate the impact of VR activities on users under lockdown due to the Covid-19 pandemic. An online survey was carried out to investigate the recreational use of VR during the lockdown period and to gather users' opinions on its impact on their physical and mental health. Non-parametric tests were used to evaluate the statistical significance of the responses provided by the 646 participants. The results of the survey show that VR use has significantly increased during the lockdown period for most participants, who expressed overwhelmingly positive opinions on the impact of VR activities on their mental and physical wellbeing. Strikingly, self-reported intensity of physical activity was considerably more strenuous in VR users than in console users. Given the current uncertainty as to the duration and course of the pandemic, as well as the possibility of intermittent lockdown in the upcoming years, the outcomes of this study could have a significant impact towards the development and deployment of VR-based strategies aimed at helping the population cope with prolonged social distancing, with particular regards to vulnerable individuals.

Development of IoT-based mhealth framework for various cases of heart disease patients

Abstract: A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

Healthcare scheduling in optimization context: a review

Abstract: This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review.

Abstract: COVID-19 had led to severe clinical manifestations In the current scenario, 98 794 942 people are infected, and it has responsible for 2 124 193 deaths around the world as reported by World Health Organization on 25 January 2021 Telemedicine has become a critical technology for providing medical care to patients by trying to reduce transmission of the virus among patients, families, and doctors The economic consequences of coronavirus have affected the entire world and disrupted daily life in many countries The development of telemedicine applications and eHealth services can significantly help to manage pandemic worldwide better Consequently, the main objective of this paper is to present a systematic review of the implementation of telemedicine and e-health systems in the combat to COVID-19 The main contribution is to present a comprehensive description of the state of the art considering the domain areas, organizations, funding agencies, researcher units and authors involved The results show that the United States and China have the most significant number of studies representing 4211% and 3158%, respectively Furthermore, 35 different research units and 9 funding agencies are involved in the application of telemedicine systems to combat COVID-19

The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures.

Abstract: This article addresses the topic of shared responsibilities in supply chains, with a specific focus on the application of the Internet of Things (IoT) in e-health environments, and Industry 4.0 issues-concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. In this article, we critically review methodologies and guidelines that have been proposed to approach these ethical aspects in digital supply chain settings. The emerging framework presents new findings on how digital technologies affect vaccine shared supply chain systems. Through epistemological analysis, the article derives new insights for transparency and accountability of supply chain cyber risk from Internet of Things systems. This research devises a framework for ethical awareness, assessment, transparency and accountability of the emerging cyber risk from integrating IoT technologies on shared Covid-19 healthcare supply chain infrastructure.

Comparing different feature selection algorithms for cardiovascular disease prediction

Abstract: Determining the key features for the best model fitting in machine learning is not an easy task. The main objective of this study is to accurately predict cardiovascular disease by comparison among different feature selection algorithms. This study has employed a two-stage feature sub-set retrieving technique to achieve this goal: we first considered three well-established feature selection (filter, wrapper, embedded), and then, a feature sub-set was extracted using a Boolean process-based common “True” condition from these three algorithms. To justify the comparative accuracy and define the best predictive analytics, the well-known random forest, support vector classifier, k-nearest neighbors, Naive Bayes, and XGBoost model have been considered. The artificial neural network (ANN) has been considered as the benchmark for further comparison with all features. The experimental outcomes exhibit that the XGBoost Classifier integrated with the wrapper methods offers precise prediction results for cardiovascular disease. The proposed approach can also be applied in other domains such as sports analytics, bio-informatics, and financial analysis in contrast with healthcare informatics. This empirical study’s novelty is that the common “True” condition–based feature selection and comparison technique is entirely a new phenomenon in medical informatics.

Fuzzy based expert system for diagnosis of coronary artery disease in nigeria.

Abstract: Expert system is an artificial intelligence based system that imitates the decision making ability of human and it is used as the diagnostic tool for many diseases including diabetes mellitus, COVID-19, cancers, coronary artery disease (CAD), among other diseases. Even though CAD is globally one of the deadliest diseases and it is not well known in Nigeria, it causes many deaths as such in 2014, 53,836 or 2.82% of total deaths in Nigeria resulted from the CAD. In this study, fuzzy based expert system for diagnosis of CAD is developed in order to provide the complementary diagnostic tools for diagnosis of CAD's patients in Nigeria. The improved C4.5 data mining algorithm is used to transfer the knowledge of human expert to the knowledge base on the expert system instead of using conventional techniques such as interviews, questionnaires, etc. Taken together, the performance evaluation system was carried out, and the system has an overall accuracy, sensitivity and specificity of 94.55%, 95.35% and 95.00% respectively; which show that, the system is reliable and capable of diagnosing both negative and positive cases of CAD patients efficiently.

Multilayer perceptron based deep neural network for early detection of coronary heart disease

Abstract: Coronary heart disease leads to a high mortality rate worldwide. Owing to delays in its detection, its treatment becomes challenging with little chances of recovery in many cases. An efficient, early-stage detection method is therefore urgently needed. Using the Framingham Heart Study Dataset, this study shows how data pre-processing via the multilayer perceptron following a deep learning approach will improve data quality when computing the likelihood of one having coronary heart disease. Apart from being highly efficient, our proposed approach results in highaccuracy of 96.50%. Finally, the paper discusses the rise in efficiency and accuracy achieved via use of deep learning techniques to enhance predictive outcomes v. traditional ones. The proposed study attempts to detect Coronary Heart Disease at an early stage.

Factors of healthcare robot adoption by medical staff in Thai government hospitals

Abstract: The patients increasing number and growing shortage of medical staff are acute problems that face the healthcare industry today. Healthcare robots are being installed to solve this problem, since they have sufficient potential to solve the problems. The healthcare robot initiative success is not only based on the executives’ decisions and robot designers but also on medical staff members’ willingness to adopt healthcare robots. Nowadays, there are gaps in our understanding about the evaluation of staff changes in using robots. This study investigated the factors involved in the robots using in Thai government hospitals based on the results of 466 questionnaire respondents. The medical staff was selected randomly for data collection. The Confirmatory factor analysis (CFA) and a structural equation modeling (SEM) are tools used in data analysis. The findings confirmed that all four UTAUT constructs of the study, namely, the facilitating conditions, social influence, effort expectancy, performance expectancy, and concerns about safety, significantly predicted the use of robots (p < .01). Medical practitioners under 35 years of age tended to accept the technology better than their more senior counterparts. The staff’s intentions and facilitation of support played a key role in adopting and using robots. Lack of technical knowledge was perceived as a barrier to technology adoption. The results also indicate a significant negative effect in the relationship between the medical staff’s behavioral intention and barrier/resistance to the healthcare robot using. This study also identifies key factors for medical staff to make acceptance decisions in relation to healthcare robots.

Exploring the acceptance of telehealth within palliative care: A self-determination theory perspective

Abstract: The aim of this paper was to explore potential divergence and convergence in relation to health care professionals’ and patients’ acceptability of the use of telehealth within palliative care provision through the lens of Self-Determination Theory. The research utilized a deductive qualitative approach utilizing semi-structured interviews to explore divergence and convergence between health care professionals’ preconceptions of the use of telehealth in palliative care and the lived experiences of patients accessing support in this manner. Semi-structured interviews were conducted with both professionals and patients to explore whether the barriers and benefits of telehealth perceived by professionals corresponded to the patient’s lived experience of utilizing the technology in their palliative care. Interviews were analyzed using a deductive thematic analysis. Professionals and patients identified that the use of telehealth could satisfy the need for autonomy, however this manifested in different ways. Greater divergence was apparent between patient and professional perceptions about how telehealth could satisfy the need for relatedness and competence needs. The findings of this paper highlight how professionals preconceived concerns about the use of telehealth in relation to providing supportive palliative care may not be realized when exploring the experiences of patients accessing services through this medium. This paper highlights the important role of psychological need satisfaction when considering acceptability of telehealth, and motivation to engage in the implementation of technologically driven health services.

An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice.

Abstract: Assessing the current Artificial intelligence (AI) situation is a crucial step towards its implementation into radiology practice. The study aimed to assess radiographer willingness to accept AI in radiology work practice and the impact of AI in work performance. An exploratory cross-sectional online survey conducted for radiographers working within the Middle East and India was conducted from May–August 2020. A previously validated survey used to obtain radiographer's demographics, knowledge, perceptions, organization readiness, and challenges of integrating AI into radiology. The survey was accessible for radiographers and distributed through the societies page. The survey was completed by 549 radiographers distributed as (77.6%, n = 426) from the Middle East while (22.4%, n = 123) from India. A majority (86%, n = 773) agreed that AI currently plays an important role in radiology and (88.0%, n = 483) expected that AI would play a role in radiology practice and image production. The challenges for AI implementation in practice were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203). Participants showed high interest to integrate AI in under and postgraduate curriculum. There is excitement about what AI could offer, but education input is a requirement. Fears are expressed about job security and how radiology may work across all ages and educational backgrounds. Radiographers become aware of AI role and challenges, which can be improved by education and training.

Design and implementation framework of social assistive robotics for people with dementia - a scoping review

Abstract: Nowadays, longevity studies have become a distinguished multidisciplinary field merging with cutting-edge computer science technologies to outline innovative ideas to cater to the needs of seniors Since the global geriatric population is anticipated to rise, the number of people seeking to obtain caregiving services and wishing to be more actively engaged in life will be more apparent Therefore, seniors aspiring day-to-day special care in home settings, interested in improving their living standards can likewise benefit from an amiable companion A comprehensive search strategy was developed and selected databases were looked through with relevant keywords This scoping review qualitatively examines the literature on the use of companion robots, including both pet-like and humanoid robots, and Internet-of-Things (IoT) security, coupled with the new 5G technology for the home-based elder care From 355 full-text articles that were found, 90 articles were selected to be investigated respectively In order to ascertain their operation in the future, we discuss remaining challenges, unused opportunities, security risks and suggested remedies and suggest a dementia-centred concept and an implementation framework

Wearable devices as a valid support for diagnostic excellence: lessons from a pandemic going forward.

Abstract: Today, the use of wearable devices is continuously increasing with many different application fields. Their low-cost and wide availability make these devices proper instruments for long-term monitoring, potentially useful to detect physiological changes related to influenza or other viruses. The relevance of this aspect and the impact of such technology have become evident particularly in the last year, during COVID-19 emergency; (big) data from wearable devices (already worn by many citizens) together with artificial intelligence techniques gave birth to specific studies dedicated to quickly identify patterns discriminating between healthy and infected people. These evaluations are made on the basis of parameters measured by these devices, among which heart rate, physical activity, and sleep seem to play a dominant role. This could be extremely significant in terms of early detection and limit of contagion risk. However, there is still a lot of research to be conducted in terms of measurement accuracy, data management (privacy and security issues), and results exploitation, in order to reach an accurate and reliable solution helping the whole healthcare system particularly in epidemic events, such as the SARS-CoV-2 pandemic.

Using the Technology Acceptance Model to conceptualise experiences of the usability and acceptability of a self-management app (COPD.Pal®) for Chronic Obstructive Pulmonary Disease.

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is a long-term progressive inflammatory lung disease causing chronic breathlessness and many hospital admissions. It affects up to 1.2 million people in the UK. To help people with COPD self-manage their condition we developed, in partnership with healthcare users, a digital mobile phone app called COPD.Pal®. We report the first user feedback of COPD.Pal®, applying the Technology Acceptance Model (TAM) theoretical framework. 11 participants engaged with a click dummy version of COPD.Pal® before being asked questions relating to their experiences. A deductive, semantic, reflexive thematic analysis was conducted to analyse their individual and collective experiences. The study was registered at Clinical Trials.gov (NCT04142957). Two overarching themes resulted: Ease of Use and Perceived Usefulness. Within the former, participants discussed how they wanted flexibility and choice in how they engaged with the app; including how often they used it. Additionally, they discussed how the app layout should make it straightforward to use, whilst unanimously agreeing that COPD.Pal® provided this. Within Perceived Usefulness, participants discussed how they wanted the information they entered into the app to be useful, in addition to the app providing resources regarding COPD. Lastly, there was disagreement regarding preferences for further app development. We found that COPD.Pal® was usable and acceptable by people with COPD and TAM provided a useful theoretical framework for both structuring discussions with users and analysing their comments.

Mobile apps for self-management in pregnancy: a systematic review

Abstract: Complications during pregnancy is a major problem affecting healthcare systems which requires the efforts of both patients and healthcare practitioners. For this reason, mobile apps have been increasingly sought to support self-management during pregnancy. Although many benefits have been claimed for the inclusion of self-management mobile apps in supporting care, the domains already explored, functionalities and impacts of mobile apps for self-management in pregnancy is still not clear. A clear understanding of the health domains already explored functionalities of existing apps which have been evaluated as well as the effectiveness of these apps can help researchers and health practitioners identify areas of future needs for self-management mobile apps during pregnancy. The objective of this systematic review was to provide a narrative synthesis of the literature on the evaluation of mobile apps for self-management during pregnancy. The search was conducted on four databases: PubMed, CINAHL, Scopus and EMBASE. 18 articles met the inclusion criteria. Nine randomised controlled trials (RCTs), one non-randomised controlled trial (NRCT) and eight observation studies evaluating self-management mobile apps among pregnant women were identified. Mobile apps for self-management have been developed with different functionalities addressing various areas of complications during pregnancy including gestational diabetes, preeclampsia and high blood pressure. These apps have also been evaluated in countries mostly in the developed context. We conclude that there have been positive impacts of mobile apps for self-management during pregnancy; however, future research should focus on evaluating mobile apps for self-management during pregnancy within developing countries as well as the use of mobile apps for the identification of sexually transmitted infections, early warning signs of potential still birth, miscarriage and management of anaemia during pregnancy.

On the value of deep learning for diagnosing diabetic retinopathy

Abstract: Diabetic retinopathy (DR) is one of the main causes of vision loss around the world. The early diagnosis of this disease can help in treating it efficiently. Deep learning (DL) is rapidly becoming the state of the art, leading to enhanced performance in various medical applications such as diabetic retinopathy and breast cancer. In this paper, we conduct an empirical evaluation of seven convolutional neural networks (CNN) architectures for an automatic binary classification of the referable diabetic retinopathy; the DL architectures (Inception_ResNet_V2, Inception_V3, ResNet50, VGG16, VGG19, MobileNet_V2 and DenseNet201) were evaluated and compared in terms of accuracy, sensitivity, specificity, precision and F1-score using the Scott Knott test and the Borda count voting method. All the empirical evaluations were over three datasets: APTOS, Kaggle DR and the Messidor-2, using a k-fold cross validation method. Experiments showed the importance of using deep learning in the classification of DR since the seven models gave a high accuracy values. Furthermore, DenseNet201 and mobileNet_V2 were the top two performing techniques respectively. DenseNet201 provided the best performance for the Kaggle and Messidor-2 datasets with an accuracy equal to 84.74% and 85.79% respectively. MobileNet_V2 provided the best performance in the APTOS dataset with an accuracy equal to 93.09%. As for the ResNet50, Inception_V3 and Inception_ResNet_V2, they were the worst performing compared to the other DL techniques. Therefore, we recommend the use of DenseNet201 and MobileNet_V2 for the detection of the referable DR since they provided the best performances on the three datasets.

Alternative mental health therapies in prolonged lockdowns: narratives from Covid-19.

Abstract: Identify and review alternative (home-based) therapies for prolonged lockdowns. Interdisciplinary study using multi-method approach - case study, action research, grounded theory. Only secondary data has been used in this study. Epistemological framework based on a set of digital humanities tools. The set of tools are based on publicly available, open access technological solutions, enabling generalisability of the findings. Alternative therapies can be integrated in healthcare systems as home-based solutions operating on low-cost technologies.

China health technology and stringency containment measures during COVID-19 pandemic: A discussion of first and second wave of COVID-19.

Abstract: This short communication highlights the Chinese health and stringency containment measures in the background of technology deployment and development during the COVID-19 pandemic in China. By achieving the study objective, this communication takes Health Containment Index and Stringency Response Index as independent variables and COVID-19 new confirmed cases as the dependent variable in the period January to October 2020. Applying simple linear regression analysis and china's technological revolution shows that china's 5G technology in the containment policies and medical support played a vital role in combat the first wave of COVID-19. These measures have remained sustainable and consistent, which made China resumption the economy and state development affairs. Furthermore, the second wave of COVID-19 was also under control due to sustainable policy enforcement during the first wave. In strengthening the health system and e-government system, China's 6G successful invention will make china's institutional structure to the next level and sustainable in combating future calamities and projected forthcoming waves of COVID-19.

mHealth technology utilization in the Arab world : a systematic review of systems, usage, and challenges

Abstract: The rapid growth in mobile technology has provided an opportunity for the design and development of mobile health technologies in the Arab region. Nonetheless, available literature has not been able to provide information on the types of systems, use patterns and challenges faced during the implementation of mobile Health (mHealth) systems in the Arab countries. This lack of evidence-based study to classify mHealth technologies, use and possible obstacles has an important role in the continuous development, implementation and future research trends of mHealth technologies in the Arab world. This study filled the gap by way of a systematic review of previous studies conducted within a decade from seven online databases to explore the current evidence on the use of mHealth in the Arab countries. The findings from a systematic review of 31 studies classified the main mHealth systems into four categories: self-healthcare management systems, assisted healthcare systems, supervised healthcare systems and continuous monitoring systems. Self-healthcare management systems were the dominant mHealth solutions while continuous monitoring systems were the least utilized. Generally, there was a low usage level of m-health systems in the Arab world underpinned by challenges such as User interface (UI), cloud storage, platforms, quality of service (QoS), security and data acquisition.

Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques

Abstract: Coronary Heart Disease (CHD) is one of the major causes of morbidity and mortality worldwide. According to the World Health Organization (WHO) survey, Cardiac arrest accounts for more deaths annually than any other cause. But the silver lining over here is that heart related diseases are highly preventable, if simple lifestyle modifications are carried out. However, it is a challenging factor to identify high risk heart patients at times due to other comorbidity factors such as diabetes, high blood pressure, high cholesterol and so on. Hence it is needed to develop an efficient early prediction model which can detect high risk patients and their life could be saved. The proposed system helps to identify the best set of features for diagnosis using traditional machine learning algorithms along with modern Gradient Boosting approaches. Genetic algorithm for feature selection to optimize performance by reducing the number of parameters by 20% whilst keeping the accuracy of the model intact is implemented in the proposed system. In addition, hyper parameter optimization techniques are executed to further improve the predictive model’s performance.

A comparative study and analysis of LSTM deep neural networks for heartbeats classification

Abstract: Heart diseases and their diagnosis has become a predominant topic in Healthcare systems as the heart is one of the pivotal parts of the human body. Electrocardiogram (ECG) signal-based diagnosis and classification have been experimented with various computational techniques which have demonstrated early detection and treatment of heart disease. Deep learning (DL) is the current interest of different Healthcare applications that includes the heartbeat classification based on ECG signals. There are various studies conducted with different DL models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) for the heartbeat classification using MIT-BIH arrhythmia dataset. This paper aims to provide a comprehensive analysis of Long-Short Term Memory (LSTM) based DL models with multiple performance metrics on the MIT-BIH arrhythmia dataset for the heartbeat classification. The different variants of the LSTM DL model are proposed for the purpose of the classification. Among the variants, the bi-directional LSTM DL model shows high accuracy in the classification of Normal beats (97%), Premature ventricular contractions (PVC) beats (98%), Atrial Premature Complex (APC) beats (98%), and Paced Beats (PB) beats (99%). The comparative analysis of the bi-directional LSTM DL model with the existing works shows 95% sensitivity and 98% specificity in the classification of heartbeats. The results evidently show that the LSTM DL models are appropriate for the classification of heartbeats.

Applying software-defined networking to support telemedicine health consultation during and post Covid-19 era.

Abstract: The novel coronavirus disease-19 (COVID-19) infection has altered the society, economy, and entire healthcare system. Whilst this pandemic has presented the healthcare system with unprecedented challenges, it has rapidly promoted the adoption of telemedicine to deliver healthcare at a distance. Telemedicine is the use of Information and Communication Technology (ICT) for collecting, organizing, storing, retrieving, and exchanging medical information. But it is faced with the limitations of conventional IP-based protocols which makes it challenging to provide Quality of Service (QoS) for telemedicine due to issues arising from network congestion. Likewise, medical professionals adopting telemedicine are affected with low QoS during health consultations with outpatients due to increased internet usage. Therefore, this study proposes a Software-Defined Networking (SDN) based telemedicine architecture to provide QoS during telemedicine health consultations. This study utilizes secondary data from existing research works in the literature to provide a roadmap for the application of SDN to improve QoS in telemedicine during and after the COVID-19 pandemic. Findings from this study present a practical approach for applying SDN in telemedicine to provide appropriate bandwidth and facilitate real time transmission of medical data.

An objective analysis of quality and readability of online information on COVID-19.

Abstract: High quality, readable health information is vital to mitigate the impact of the COVID-19 pandemic. The aim of this study was to assess the quality and readability of online COVID-19 information using 6 validated tools. This is a cross-sectional study. "COVID-19" was searched across the three most popular English language search engines. Quality was evaluated using the DISCERN score, Journal of the American Medical Association benchmark criteria and Health On the Net Foundation Code of Conduct. Readability was assessed using the Flesch Reading Ease Score, Flesch-Kincaid Grade Level and Gunning-Fog Index. 41 websites were suitable for analysis. 9.8% fulfilled all JAMA criteria. Only one website was HONCode certified. Mean DISCERN score was 47.8/80 ("fair"). This was highest in websites published by a professional society/medical journal/healthcare provider. Readability varied from an 8th to 12th grade level. The overall quality of online COVID-19 information was "fair". Much of this information was above the recommended 5th to 6th grade level, impeding access for many.

Telemedicine curriculum in undergraduate medical education: a systematic search and review

Abstract: The number of patient-doctor appointments carried out using telemedicine has surpassed in-person appointments. In spite of this, it is unclear that telemedicine curricula in undergraduate medical education reflect the real importance by means of the effectiveness of these approaches. We aimed to systematically search and review the studies that are on undergraduate telemedicine curricula. We searched the Web of Science, PubMed, and Scopus using the keywords such as telemedicine, medical education, and curriculum. Our search was limited to publication dates between January 1, 2000, and February 1, 2020. We elicited the information of the curricula as to their countries, participants, aims or objectives, teaching methods, and evaluation of effectiveness. We also evaluated the quality of the studies using the Joanna Briggs Institute Qualitative Appraisal and Review Instrument. Out of 461 studies, seven articles were selected based on selection criteria for further review. The studies were mostly from the USA. The participant numbers were between seven and 268. There were several modes of delivery but lectures and patient encounters were used mostly. In four studies, the effectiveness was evaluated only by using satisfaction surveys, and the results were satisfactory. A study reported the acquisition and application of skills as a result. There is no well-established telemedicine curriculum in the undergraduate years. The methods vary but the effectiveness of the educational programs does not have a robust evidence base. It is evident that undergraduate medical education needs a curriculum backed by strong scientific data on its effectiveness.

Systematic review of training environments with motor imagery brain–computer interface: Coherent taxonomy, open issues and recommendation pathway solution

Abstract: The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes.