How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

The Impact of Big Data on Chronic Disease Management

Introduction: Population health management – and specifically chronic disease management – depend on the ability of providers to identify patients at high risk of developing costly and harmful conditions such as diabetes, heart failure, and chronic kidney disease (CKD). The advent of big data analytics could help identify high-risk patients which is really beneficial to healthcare practitioners and patients to make informed decisions in a timelier manner with much more evidence in hand. It would allow doctors to extend effective treatment but also reduces the costs of extending improved care to patients. Purpose: The purpose of this study was to identify current applications of big data analytics in healthcare for chronic disease management and to determine its real-world effectiveness in improving patient outcomes and lessening financial burdens. Methodology: The methodology for this study was a literature review. Six electronic databases were utilized and a total of 49 articles were referenced for this research. Results: Improvement in diagnostic accuracy and risk prediction and reduction of hospital readmissions has resulted in significant decrease in health care cost. Big data analytic studies regarding care management and wellness programs have been largely positive. Also, Big data analytics guided better treatment leading to improved patient outcomes. Discussion/Conclusion: Big data analytics shows initial positive impact on quality of care, patient outcomes and finances, and could be successfully implemented in chronic disease management

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canad

Big Data in Sports: A Bibliometric and Topic Study

Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic