Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Social Media and Healthcare Technology in 2022: Macro analytics, text and data mining, social media platforms and COVID-19

Social media has changed the way individuals and institutions approach healthcare and health information and offers opportunities to understand health-related interactions at all levels, from the micro to the macro. The Social Media and Healthcare Technology mini-track presents research papers that address a diverse array of social media and associated technology within healthcare and healthcare research;including macro analytics, text and data mining and the role of social media platforms and influencers in health care and health-related decision making. © 2022 IEEE Computer Society. All rights reserved.

Technological, healthcare and consumer funds efficiency: influence of COVID-19

This paper aims to analyze the efficiency of the funds in technological, healthcare, and consumer cyclical sectors based on the U.S. News & World Report rankings. We employed a Principal Component Analysis to select the indicators to explain efficiency. Then, we have used an alternative approach that combines Data Envelopment Analysis (DEA) with Multiple Criteria Decision Aiding, the Value-Based DEA, to assess the efficiency of funds for 1 year (2020), 3 years (2018–2020), and 5 years (2016–2020). The results highlight that in 2020 the number of efficient funds is much smaller than in previous periods and this can be justified by the effect of the COVID-19 pandemic crisis. The sectors with the most efficient funds are technology and healthcare. The factors that determine the efficiency of funds in the health sector and the technology sector are quite different, although they have not undergone major changes in the three periods considered. For managers, health funds are seen as low risk and hardly consider the return factors in all analyzed periods, which is often considered as benchmarks for inefficient funds. In the technology sector, Beta and Alpha are generally the indicators with the greatest weight in fund efficiency, showing that these funds beat the market in terms of returns and are less risky than the benchmark. This study seeks to complete the scarce existing literature on the subject, namely in the sectors under analysis, seeking to identify the indicators that fund managers ponder most to consider a fund as efficient. As far as we know, the joint efficiency analysis of these sectors and the impact they suffered from the COVID-19 pandemic are new in the literature.

Global Perception of Telemedicine before and after COVID-19 : A Text Mining Analysis

Determining the perception and sentiment of public's opinion of telemedicine and telecare has benefits to healthcare organizations, physicians and patients. Determining a relationship between opinion and demographic elements will aid in developing ways to close the gap between perception and readiness to implement healthcare technology for patients. The concept of telemedicine becomes more critical due to the onset of pandemics such as COVID-19. In addition, with telemedicine being a viable option to reduce cost and inconvenience for the patient, while delivering care that is effective and efficient, having patient buy in will be a key element. This study aims to identify the perception about telemedicine and telecare based on the posts by Twitter users eighteen months before and after COVID-19 pandemic. We leveraged VADER sentiment analysis model to identify the sentiment of the public using the tweets they posted. Out of approximately 1,073,817 tweets included, 491,695 unique tweets from 10,495 unique users met the inclusion criteria Among all countries, United States dominated the tweet volume. Among all the states in US, it is interesting to note that district of Columbia dominated the tweet volume. Among tweets from top five English speaking countries, interestingly after March 2020, the average sentiment of all countries seems to converge to the same value. Results indicate that before COVID-19 outbreak, people had neutral perception or sentiment towards telemedicine, while after the onset of increased cases and high alert situations, people tend to support Telemedicine and the overall perception started to grow towards the positive side. © 2022 IEEE.

A novel medical image enhancement technique based on hybrid method

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.

Educational Assistant Wireframe for the Elderly to Mitigate Urban Climate Health Risks

The climate in urban centers can differ significantly from the immediate surrounding areas;this can pose health risks to the elderly who have spent much of their lives in urban centers and then move to more rural areas for retirement. Therefore, there is a need to develop educational software applications for the elderly that needs to account for their life experiences aside from physiological restrictions. This research created a connected wireframe for an educational assistant to make the elderly aware of the climate differences between their urban and rural residences. To address the difficulty of recruiting vulnerable subjects, especially during the COVID-19 pandemic, we evaluated our wireframe using a combination of heuristic evaluation and a variation of the Delphi process, an expert consensus-building tool typically used for market forecasting. © ICCE 2022.All rights reserved.

An Insight into Wearable Devices for Smart Healthcare Technologies

Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.

The Impact of the COVID-19 Pandemic on Quality Education of the Medical Young Generation.

(1) Generating the need to impose social distancing to reduce the spread of the virus, the COVID-19 pandemic altered the ways in which the teaching process normally happens. The aim of our study was to determine the impact of online teaching on medical students during this period. (2) Our study included 2059 medical, dental and pharmacy students from the University of Medicine and Pharmacy "Grigore T. Popa", Iasi, Romania. We used a modified metacognition questionnaire after translation into Romanian and validation. Our questionnaire included 38 items, and it was divided into four parts. Academic results and preferences regarding the on-site or online courses, information regarding practical training, self-awareness in terms of one's feelings such as anger, boredom and anxiety and also substance use linked to online teaching, and contextualization of the relationship with colleagues, teachers, friends and family were among the most important points evaluated. A comparison was made between preclinical and clinical students. A five-item Linkert-like scale was used for rating the answers in the last three parts that evaluated the impact of the SARS-CoV-2 pandemic on the educational process. (3) Preclinical medical students, compared to preclinical dental students, obtained statistically significant improvements in their evaluation results, with fewer failed exams (p < 0.001) and with similar results being obtained by comparing dental with pharmacy students. All students obtained statistically significant improvements in their academic results during the online evaluation. A statistically significant increase in anxiety and depression with a p-value of <0.001 was registered among our students. (4) The majority found it difficult to cope with this intense period. Both teachers and students found it difficult to adjust on such short notice to the challenges posed by the new concept of online teaching and learning.

Dynamic Modeling of a Low-cost Mechanical Ventilator

In this paper, we develop a dynamic model for a low-cost ventilator, Novavent, developed at Villanova University. Since 2020, several academic institutions have focused on developing a less expensive ventilator system with the goal of increasing accessibility of medical ventilation for low-income countries. This is mainly a response to the ongoing Covid-19 pandemic. Due to the demand the pandemic has created, the development process of these new designs have prioritized development and implementation speed over model validation. In order to increase the understanding and aid in the controller design process of these new ventilator designs, an empirical model was developed for a low-cost ventilator design, developed at Villanova University. The process of this model design was systematically documented with the intention that other institutions can use this process as a template for modeling their own designs. After developing expressions of the different responses in the system observed through empirical data, Simulink was then used to combine all expressions into a holistic model. This model was then validated using data collected from the response of the ventilator design observed. © 2022 Elsevier B.V.. All rights reserved.

The Effectiveness of Remote Consultations During the COVID-19 Pandemic: A Tool for Modernising the National Health Service (NHS).

Objective The coronavirus disease 2019 (COVID-19) pandemic prompted major changes to the delivery of care. There was a move towards remote consultations in order to mitigate the risk of viral exposure and the risk of delaying care. Remote consultations will play a prominent role within the National Health Service (NHS) in the future. This project aimed to evaluate the effectiveness of remote consultations relative to face-to-face (F2F) consultations. Methods A local retrospective audit of remote consultations in ENT was performed by comparing outcome data for video and telephone appointments during the first peak of the pandemic to outcomes for F2F consultations during the same months of the preceding year. Chi-square tests were employed to determine whether there was any statistically significant discrepancy between the two modalities. Results Outcomes from a total of 314 patient consultations were reviewed. One hundred and fifty-four patients were male, and 160 were female; 111 patient consultations were conducted F2F, and 203 remotely (101 via telephone and 102 via video). There was no statistically significant difference detected between remote and F2F groups for rates of investigation, listing for theatre, referral to other specialties, and initiating treatment. Patients reviewed remotely were less likely to be discharged than those reviewed F2F (p=<0.001). Comparing the two remote modalities, telephone patients were more likely to undergo investigation than patients reviewed over video (p = 0.031). Conclusions Remote consultations were an effective and reliable resource for maintaining a high standard of care during the COVID-19 pandemic. Our findings suggest that remote consultations will prove a valuable tool for clinicians in the remobilisation of health services in the post-pandemic era.

A Critical Review of Global Digital Divide and the Role of Technology in Healthcare.

Healthcare and technology, the fusion of these two distinct sciences can be traced back to the Vedic era. Regrettably, while it is evident that the journey of advancements in knowledge and innovation leading to the advent of technology to better the health of mankind is not a recent one, owing to inexistent means of transfer of knowledge, these contraptions stayed mostly localized to the regions of their inventors. This article seeks to review the vital role that technology has in bettering the health status of the global community and the challenges associated with healthcare technologies like inequity in connectivity, affordability, and accessibility. Technology and artificial intelligence are integrated to the best of the health systems across the world but these advancements are not accessible to a considerable part of the global population. While affordability, the absence of a steady internet supply, and the lack of a device to use the technology are the major impediments causing this digital divide, cultural factors and health literacy also contribute to this scenario. Nevertheless, access to the internet has been recognized as a basic need by all governments around the globe. The COVID-19 pandemic shook the health systems of developed and developing countries alike and has made every administration feel the urgency in making healthcare more accessible. Having seamless internet coverage and setups to make telemedicine or online consultations possible, can contribute significantly in paving the path to making our societies prosperous and healthier. With the world's consensus about this goal, efforts now should be focused on research and development for making these technologies more affordable and accessible without compromising their utility.

User involvement throughout development of technology to support diabetes foot care: a case illustration of the devices for dignity PPIE model.

The successful development and implementation of any healthcare technology requires input from multiple stakeholders including clinical leads, trust information technology directorates as well as project management and procurement. In this process however, a key stakeholder that is often overlooked is the patient.This paper illustrates the crucial importance of patient involvement to avoid poor design and poor uptake of technology and subsequently poor health outcomes.To highlight this, we share a case example evidencing involvement of people with lived experience of foot ulcers resulting from Diabetic foot neuropathy throughout identification of unmet technology needs, design requirements for the device and iterative device development and evaluation.

Visualization of Global Research Output on Disruptive Innovation in Health Care

The outbreak of COVID-19 has raised concerns about the availability of health care facilities globally. Disruptive innovations in health care may impact a new system that provides a continuum of treatment tailored to each patient’s specific requirements. In light of this evolution, this study aimed to visualize global research output on disruptive innovation in health care between 2001 to 2021 as indexed in the Scopus database. The dataset was extracted on January 10, 2022, and 204 records were identified for data analysis. Various bibliometric indicators were used to identify publication trends. VOSviewer visualization software was also used to analyze data. The findings revealed the increasing pattern of publication growth with slight fluctuation over time. M. Friebe was the most prolific author having contributed four publications. The Harvard Medical School was the most productive institution with eight publications and the United States was the most productive country with 84 publications on disruptive innovation in health care. Furthermore, human, health care, and disruptive innovation were the top keywords in this field. These findings are expected to be useful to academics and administrators all across the world. This study also gives readers insight into this domain and will allow them to begin their research by selecting a topic of their choice. [ FROM AUTHOR] Copyright of Journal of Hospital Librarianship is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Model-Driven Optical Clinic Management Systems: Systematic Software Engineering Approach

INTRODUCTION: eHealth systems in a modern hospital and clinic require stringent measures to coordinate the operations of doctors, nurses, pharmacies for improved health care delivery. OBJECTIVES: The primary objective is to perform a comparative analysis to devise a novel approach to address the needs of a hospital information management system. This has triggered an urgent response to develop Optical Clinic Management System (OCMS) to address the limitation of the existing system. This intervention would promote the good health and well-being of humankind to meet the Sustainable Development Goals 3 (SDGs 3). METHODS: The study proposed a Design Science Research Methodology (DSRM) approach in Software Engineering as a catalyst to design OCMS to capture patients’ up-to-date records for medical diagnosis. The system is to assist Clinicians to prescribe medications based on a patient’s medical history by clicking a computer button. RESULTS: The limitations discovered during systems analysis and design of the existing systems were addressed during system evaluation and testing. It was observed that the proposed optical clinic management systems received a 98% acceptance for the implementation. CONCLUSION: This study explores the problem facing clinic and hospital administration and established major factors affecting the existing systems. It was discovered that the paper-based management systems used to keep patients’ medical records were found to be unreliable and therefore unsafe to be used as the basis to prescribe medication for patients, hence the need for this comprehensive system to address the problem for effective health care delivery. The situation in the existing system incidentally led to misplaced and unstructured handling of patient clinical records that may inadvertently make the clinicians administer medications with no reference to the patient’s previous diagnosis due to the lost file. Hence, the aftermath of the Covid-19 pandemic and its global destruction of human lives should motivate African leaders to invest adequate resources in the development of information technology applications for robust health information systems to improve health care delivery in Africa. © 2022 Adams Addison Kobla Azameti et al.,.

Low-Cost Smart COVID-19 Patient Monitoring and Support System

Social distancing, isolation, and quarantining are very familiar words since the outbreak of the coronavirus (COVID-19). COVID - 19 is a highly contagious pathogenic viral infection. It is very risky to get close contact with people who have COVID-19 symptoms or COVID-19 positive;nevertheless, covid patient monitoring is also significant for saving his/her life. To solve the Covid-19 pandemic situation accentuates a focus on remote patient monitoring. A small smart healthcare support system is built to monitor COVID-19 patients' health status and the patient emergency abet. This system can also trace the patient location;thus, aid can be provided to the patient promptly. This system uses a respiration sensor, oxygen saturation sensor, temperature sensor, heart rate sensor, GPS. All the sensors, as well as GPS, are connected with Arduino-Uno. By processing sensor data, the smart system can discern the patient's critical condition and forward this information to the doctor/nurse or hospital in charge and patient relative's smartphone as a text message. This paper aims to develop a system to support COVID-19 patients and develop a remote healthcare platform for monitoring pandemic situations and providing emergency aid promptly as a text to the smartphone. © 2021 IEEE.

Using virtual reality in palliative care: a systematic integrative review.

BACKGROUND: Recently, healthcare services have witnessed an exponential increase in the use of immersive and non-immersive virtual reality (VR) technology to improve health-related outcomes. However, the use of VR in palliative care remains relatively unexplored. AIMS: To review and synthesise evidence regarding the experiences of patients, families and healthcare professionals in palliative care who have engaged with immersive/non-immersive VR technology. METHODS: A systematic integrative review using pre-defined MeSH search terms to identify eligible studies from five electronic databases (Cochrane Library, CINAHL, OVID Medline, Pubmed and Scopus) between April 2020 and February 2021. FINDINGS: In total, 1066 articles were reviewed, 55 articles were considered eligible and subject to further analysis and a total of 16 articles met the inclusion criteria and were subject to critical appraisal. Rigorous analysis of eligible articles resulted in the identification of five overarching and interconnected themes: connection, VR as an emergent technology, perceptual change, safety, and future research. CONCLUSION: This review identified that VR could support patients, families and healthcare professionals in palliative care. As a result of the COVID-19 pandemic, the findings could prove particularly significant for facilitating connection. However, further research is necessary to explore the full scope of VR use in this speciality.

Security in Big Data Health Care System

There has been a fundamental shift in the way firms in every industry manage, examine, and utilize their data. Health care is one of the most promising industries in which the use of big data may make a positive impact. Healthcare technology is being improved at a fast rate as an outcome of growing information and innovative innovation. In healthcare, there are different articles of big data. Digital medical data, biometric data, medical image processing, biosensor data, physician data, patient information, and administrative data are examples of these types. Many combined technologies are being deployed to modify healthcare systems in the COVID-19 pandemic. The security of medical data is required for the management of an integrated healthcare solution. In this paper, we found that many researchers face significant hurdles in encrypting sensitive patient information to prevent misuse or leakage. Our aim is to provide a focus on security issues in healthcare system and try to give a solution. © 2021 IEEE.

Artificial Intelligence in the Fight Against Covid-19 (Coronavirus)

Advanced healthcare technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and deep learning, are required to counter and even prepare for new illnesses. As a result, we are examining IA's capacity to control and manage COVID-19 (Coronavirus) and other emerging pandemics. Using COVID-19 or Coronavirus and Artificial Intelligence or AI keywords, the material may be quickly found in the PubMed database. COVID-19 AI's existing understanding was analyzed to see how it may be used to increase COVID-19 AI's overall usefulness. Seven COVID-19 pandemic-related AI applications have been documented. The technology has the potential to locate the infection, track it through the system, and make forecasts about when the virus will infiltrate the whole system again. Decision-making tools are desperately needed to help combat this outbreak and allow healthcare institutions to gather enough information in real time to halt its spread. The primary objective of AI is to mimic human thinking using an expert methodology. COVID-19 vaccination production may also play a critical part in making sense of and advocating a similar project. This kind of technology is helpful in screening because of its emphasis on discoveries. © 2021 IEEE.

Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic.

Technological interventions in the healthcare sector, namely, telemedicine services, have helped the government and people in these extraordinarily challenging times of ongoing COVID-19 pandemic. We identify and group key success factors relevant to telemedicine services under 7 contextual criteria. Furthermore, we explore the causal relations among them using the decision-making trial and evaluation laboratory (DEMATEL) method. Then, by applying the Bayesian best-worst method (BWM), we compute the relative importance of these criteria. Thereafter, we rank six hospitals that have provided telemedicine services through a comparative evaluation using the VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method. The threefold findings of our study reveal that (i) the technological criteria provide the highest causal impact, while the environmental criteria provide the least causal impact. (ii) The hierarchical model of criteria, achieved through the Bayesian BWM score, shows that the criteria weights for both technological and organizational criteria are maximum (0.205) and minimum (0.087), respectively. (iii) The evaluation of six hospitals with VIKOR based on seven criteria ranks the Himalayan hospital as first, showing that it is best in providing telemedicine services to patients. Public health policymakers could use the results of our study to devise an effective plan for patient care in crisis, like COVID-19.

EVision: Epidemic Forecasting on COVID-19

Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. However, supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered potentially regularly. Hence, the prediction of the rate of growth of COVID-19 cases is paramount to ensuring the ample supply of vaccines as well as for local, state, and federal government measures to ensure the availability of hospital beds, supplies, and staff. eVision is an epidemic forecaster aimed at combining Machine Learning (ML) - in the form of a Long Short-Term Memory (LSTM) Recursive Neural Network (RNN) - and search engine statistics, in order to make accurate predictions about the weekly number of cases for highly communicable diseases. By providing eVision with the relative popularity of carefully selected keywords searched via Google along with the number of positive cases reported from the US Centers for Disease Control and Prevention (CDC) and/or the World Health Organization (WHO) the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends. Thus, in order to predict the trend of the outbreak in a specific region, eVision is provided with a weekly count of the number of COVID-19 cases in a region along with statistics surrounding common symptom search phrases such as "loss of smell"and "loss of taste"that have been searched on Google in that region since the start of the pandemic. eVision has, for instance, been able to achieve an accuracy of %89 for predicting the trend of the COVID-19 outbreak in the United States © 2021 by Walter de Gruyter Berlin/Boston.