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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England. Findings: The main contributions of the presented work are the development of a conceptual BA framework and a BA dashboard tool to support management and decision making in primary care. This was evaluated through a case study of the implementation of the BA dashboard tool in London's largest primary care provider. This BA tool provides real-time information to enable simpler decision-making processes and to inform business transformation in a number of areas. The resulting increased efficiency has led to significant cost savings and improved delivery of patient care.

Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

Background: Pakistan’s immunization uptake rates are still significantly lower than anticipated despite several initiatives. Lack of awareness, forgetting about vaccination schedule, and vaccine misconception/misinformation are a few of the major drivers that mitigate the rates of immunization. The current COVID-19 pandemic emphasizes the importance of immunization. The significant reductions in regular childhood vaccination during pandemic have increased the risk of outbreaks of vaccine-preventable diseases. Concerns among parents over possibly exposing their children to COVID-19 during child visits may have contributed to the reported declines. Innovative and cost-effective mHealth interventions must be implemented in order to address the problem of inadequate immunization rates. In addition, it is also critical to understand the end user needs in order to reflect on the highly relevant essence of the customized healthcare experience. Objective: The aim of this study was to learn about caregivers’ attitudes toward the usability and acceptability of behavior-change smartphone applications (mobile phones) for improving immunization coverage in Pakistan. Methods: A mixed-method design was employed for this study. The study was conducted at Aga Khan University, Hospital. Parents visiting the Community Health Center for 6-week vaccination of their children were recruited. The study was conducted in two stages. Stage 1 consisted of qualitative interviews that grasped the parent’s attitudes and challenges to immunization, as well as their acceptability and accessibility of the smartphone-based behavior-change application to increase vaccine uptake. Stage 1 was followed by stage 2, in which data were collected through a questionnaire designed by using data from qualitative interviews. Results: The majority of participants agreed that immunization serves an important role in protecting their child from illnesses that cause morbidity and mortality. Almost all of them emphasized the importance of using a pre-appointment method at vaccination center in order to reduce the waiting time. Furthermore, participants were also interested in AI-based behavior modification applications related to immunization. They also wanted to have applications in their native language for better understanding and communication of related information. In our study, approximately 95.2 percent of participants agreed to accept SMS immunization updates, which was also reasonably high. Lastly, the majority of them identified forgetfulness as a significant contributor to regular immunization. Conclusion: To enhance the uptake of childhood vaccines, overall vaccination rates, and overcome barriers related to vaccination coverage, cost-effective and user-friendly mHealth AI-based smart phone applications are required to raise awareness regarding the continuation of vaccination service and the importance of timely vaccination. Parents’ experiences and attitudes must be considered while designing and evaluating the efficacy of mHealth-based interventions.

The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance.

BackgroundChronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the ‘fast decliner’ phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis.MethodsA prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set).ResultsThree COPD phenotypes were identified, the most common of which was the ‘fast decliner’—characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function—yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype.ConclusionsIn this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.

This paper explores the notion of the modular building construction site as an applied instance of redistributed manufacturing, in so doing, this research seeks to reduce the environmental footprint of building sites, treating them as small digitally connected subunits. In seeking to provide a whole lifecycle appreciation of a construction project, it is noted that the presence of a framework to provide guidance on the consideration of Internet of Things (IoT) data streams and connected construction objects is currently lacking. This paper proposes use of embedded IoT enabled sensing technology within all stages of a modular building lifecycle. An expanded four-phase model of intelligent assets use in construction is proposed along with an outline of the required data flows between the stages of a given building’s entire lifecycle that need to be facilitated for a BIM (Buildings Information Modelling) representation to begin to describe a building project as a sustainable asset within the circular economy. This paper also describes the use of concrete as a modular sensing structure, proposing that health monitoring of the material in situ along with the recoding of environmental factors over time could help to extend the longevity of such structures.

Background The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs. Trial Registration ClinicalTrials.gov NCT04449107; https://clinicaltrials.gov/ct2/show/NCT04449107 International Registered Report Identifier (IRRID) DERR1-10.2196/22996

Background Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Objective The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Methods This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. Results Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects. Conclusions We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

BACKGROUND Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. OBJECTIVE The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. METHODS This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. RESULTS Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects. CONCLUSIONS We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx Block-RAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution ( $1080\times 1920$ ) @103 fps.

In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.

Background Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. Methods We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. Results Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. Conclusions This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

Three key concept domains are considered and explored in a unitary framework. They are: innovation, the only possible response to global crises, aiming at transforming behaviours and practices towards systemic changes and transition; design, a way of creatively conceiving, developing and driving forward new practices for undertaking large scale transitions; and cities, seen as the environments where problems present themselves in the most socially relevant way and at the same time as key opportunities for testing and adopting forms of innovation which target global challenges. The chapter positions the three key concepts in relation to the most relevant academic references and to the current debate on innovation processes.

Globally the implementation of information and communication technology (ICT) in the healthcare sector has brought about significant improvement in the delivery of healthcare services, yet such transformation is still under way or in its infancy in many developing countries. Pakistan is a lower middle-income country (LMIC) where the health status of population and management of health systems data are very poor. In Pakistan, healthcare services are being provided by public and private sector organisations as well as by individual practitioners. Healthcare delivery and disease monitoring and evaluation are supported by health information systems (HISs) and district health information systems (DHIS). However, like in many other LMICs, implementation of HIS and DHIS is under-deployed in Pakistan mainly due to non-integration of public and private healthcare providers’ information systems. Thus there is a pressing need for exploring and facilitating the effective use and integration of public sector DHIS with the private healthcare sector in Pakistan (as well as other similar LMICs) – not only for the diagnosis, monitoring and healthcare delivery in general but also for planning and decision-making processes. Such a systemic change will usher in a new era of increased availability of high-quality accessible and relevant health information, which will be of great benefit to the population’s health.

Stakeholder engagement plays a fundamental role in the success of ‘operational research’ initiatives including simulation projects. However, there is little empirical evidence of real engagement in the context of healthcare simulation. This paper principally examines this issue and aims to provide insights into the possible causes. The paper reports on the results of a literature review and 10 field studies within the UK healthcare settings, supplemented with the authors’ experience in order to arrive at an initial list of the causes, which will then be tested through a survey of expert opinions. Twelve primary and 26 secondary causal factors, which received statistically significant level of agreement from the experts, are presented in a fish-bone diagram. The findings indicate that communication gap between simulation and stakeholder groups is the top primary factor contributing the most to the poor stakeholder engagement in healthcare simulation projects, followed by ‘poor management support’, ‘clinician’s high workload’ and ‘failure in producing tangible and quick results’.

This paper discusses the findings of research conducted between 2013 and 2016, which concerned the development of technology roadmaps for the Creative Industries. The roadmap presented in this paper was built based on input from communities of creative and Information and Communication Technologies (ICT) experts collected during the consultation and validation phases of the research. It provides a synthesis of challenges and recommendations from the five creative sectors examined by the project – Architecture, Art, Design, Games, Media and e-Publishing – and proposes research directions for the development of desired future technologies, by highlighting innovative future developments in the Creative Industries, while also assessing their technology maturity in the short, medium and longer terms. By rating the desired technologies as ‘present’ (1–2 years), ‘possible’ (2--5 years), or ‘probable’ (5–10 years or beyond), the roadmap gives orientation towards the development of new technologies and related business models and skills and provides guidance for informed policy-making. The paper thus aims at enabling stakeholders – creators, professionals, SMEs, creative groups, creative communities, associations, organisations and institutions, as well as governments and policy makers – to maximise their benefit and the societal value from the new emerging technology landscape in the Creative Industries.

Modeling and simulation (M&S) studies have been widely used in industry to gain insights into existing or proposed systems of interest. The majority of these studies focus on productivity-related measures to evaluate systems’ performance. This paradigm, however, needs to be shifted to cope with the advent of sustainability, as it is increasingly becoming an important issue in the managerial and the organizational agendas. The application of M&S to evaluate the often-competing metrics associated with sustainable operations management (SOM) is likely to be a challenge. The aim of this review is to investigate the underlying characteristics of SOM that lend towards modeling of production and service systems, and further to present an informed discussion on the suitability of specific modeling techniques in meeting the competing metrics for SOM. The triple bottom line, which is a widely used concept in sustainability and includes environmental, social, and economic aspects, is used as a benchmark for assessing this. Findings from our research suggest that a hybrid (combined) M&S approach could be an appropriate method for SOM analysis; however, it has its challenges.

This is the post-print version of the paper. The official published version can be accessed from the link below - Copyright @ 2012 IEEE This paper investigates the robust sliding mode control (SMC) problem for a class of uncertain nonlinear stochastic systems with mixed time delays. Both the sectorlike nonlinearities and the norm-bounded uncertainties enter into the system in random ways, and such randomly occurring uncertainties and randomly occurring nonlinearities obey certain mutually uncorrelated Bernoulli distributed white noise sequences. The mixed time delays consist of both the discrete and the distributed delays. The time-varying delays are allowed in state. By employing the idea of delay fractioning and constructing a new Lyapunov–Krasovskii functional, sufficient conditions are established to ensure the stability of the system dynamics in the specified sliding surface by solving a certain semidefinite programming problem. A full-state feedback SMC law is designed to guarantee the reaching condition. A simulation example is given to demonstrate the effectiveness of the proposed SMC scheme. This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 60825303 and 60834003, National 973 Project under Grant 2009CB320600, the Fok Ying Tung Education Fund under Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China under Grant 2007B4, the Key Laboratory of Integrated Automation for the Process Industry Northeastern University) from the Ministry of Education of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany.