scholarly journals Seasonal Trends in Global Dieting Online: A Big Data Survey

2019 ◽  
Author(s):  
MYUNG-BAE PARK ◽  
Jumee Wang ◽  
Bernard E. Bulwer ◽  
Chhabi Ranabhat
Keyword(s):  
Public Health ◽  
Big Data ◽  
New Approach ◽  
Seasonal Trends ◽  
Health Area ◽  
Data Survey

It is new approach using big-data in public health area. I am sure that this approach will provide valuable lessons in suggesting a policy approach.

scholarly journals Seasonal Trends in Global Dieting Online: A Big Data Survey

2019 ◽  
Author(s):  
MYUNG-BAE PARK ◽  
Jumee Wang ◽  
Bernard E. Bulwer ◽  
Chhabi Ranabhat
Keyword(s):  
Public Health ◽  
Big Data ◽  
New Approach ◽  
Seasonal Trends ◽  
Health Area ◽  
Data Survey

It is new approach using big-data in public health area. I am sure that this approach will provide valuable lessons in suggesting a policy approach.

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

scholarly journals 4.C. Round table: Joining forces: frameworks for international and multi-sectoral collaborations in health information

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Information ◽  
Population Health ◽  
Round Table ◽  
Secondary Use ◽  
Use Of Data ◽  
National Initiatives ◽  
Information Research

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.

scholarly journals The academic viewpoint on Big data and patient data ownership (as seen in the scientific literature)

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Mircheva ◽  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Medical Research ◽  
Information And Communication Technologies ◽  
Patient Information ◽  
Scientific Literature ◽  
Medical Data ◽  
Patient Data ◽  
Academic Community ◽  
Ethical Challenges

Abstract Background Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully understood. There are not enough publications on the subject. Since the topic is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies, a slightly more sophisticated analysis of the issue is needed. Aim To determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data. Methods Literature search for full text publications, indexed in PubMed, Springer, ScienceDirect and Scopus identified only 27 appropriate articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Three major aspects were studied: scientific area of publications, aspects and academicians' perception of ownership in the context of Big Data. Results Publications are in the period 2014 - 2019, 37% published in health and medical informatics journals, 30% in medicine and public health, 19% in law and ethics; 78% authored by American and British academicians, highly cited. The majority (63%) are in the area of scientific research - clinical studies, access and use of patient data for medical research, secondary use of medical data, ethical challenges to Big data in healthcare. The majority (70%) of the publications discuss ownership in ethical and legal aspects and 67% see ownership as a challenge mostly to medical research, access control, ethics, politics and business. Conclusions Ownership of medical data is seen first and foremost as a challenge. Addressing this challenge requires the combined efforts of politicians, lawyers, ethicists, computer and medical professionals, as well as academicians, sharing these efforts, experiences and suggestions. However, this issue is neglected in the scientific literature. Publishing may help in open debates and adequate policy solutions. Key messages Ownership of patient information in the context of Big Data is a problem that should not be marginalized but needs a comprehensive attitude, consideration and combined efforts from all stakeholders. Overcoming the challenge of ownership may help in improving healthcare services, medical and public health research and the health of the population as a whole.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

scholarly journals Leviathan, or the Rudder of Public Health

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-8
Author(s):  
Denis Horgan ◽  
Walter Ricciardi
Keyword(s):  
Public Health ◽  
Big Data ◽  
New Technologies ◽  
Personalised Medicine ◽  
Member States ◽  
Reform Agenda ◽  
The World ◽  
The Face ◽  
The Eu ◽  
Do So

In the world of modern health, despite the fact that we've been blessed with amazing advances of late - the advent of personalised medicine is just one example - “change” for most citizens seems slow. There are clear discrepancies in availability of the best care for all, the divisions in access from country to country, wealthy to poor, are large. There are even discrepancies between regions of the larger countries, where access often varies alarmingly. Too many Member States (with their competence for healthcare) appear to be clinging stubbornly to the concept of “one-size-fits-all” in healthcare and often stifle advances possible through personalised medicine. Meanwhile, the legislative arena encompassing health has grown big and unwieldy in many respects. And bigger is not always better. The health advances spoken of above, an increased knowledge on the part of patients, the emergence of Big Data and more, are quickly changing the face of healthcare in Europe. But healthcare thinking across the EU isn't changing fast enough. The new technologies will certainly speak for themselves, but only if allowed to do so. Acknowledging that, this article highlights a positive reform agenda, while explaining that new avenues need to be explored.

Big data and simulation – A new approach for real-time TBM steering

2019 ◽  
pp. 2681-2690
Author(s):  
G. Meschke ◽  
B.T. Cao ◽  
S. Freitag ◽  
A. Egorov ◽  
A. Saadallah ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
New Approach

scholarly journals Exploring associations between children’s obesogenic behaviours and local environment using big data (Preprint)

2020 ◽  
Author(s):  
Nicos Maglaveras ◽  
Dimitris Filos ◽  
Irini Lekka ◽  
Vasileios Kilintzis ◽  
Leandros Stefanopoulos ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Big Data ◽  
Childhood Obesity ◽  
Local Environment ◽  
Living Environment ◽  
Health Policies ◽  
Major Public Health Problem ◽  
European Cities ◽  
Custom Made

UNSTRUCTURED Obesity is a major public health problem globally and in Europe, while the prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast-food retailers, among others. Thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems to objectively measure the effect of living environment parameters on obesogenic behaviours and obesity so that tailored policies can be planned. The H2020 project “BigO: Big Data Against Childhood Obesity” (http://bigoprogram.eu) aims to tackle childhood obesity by creating new sources of evidence based on big data. This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which can support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. In particular, OPdashboard allows for (i) the real time monitoring of children’s obesogenic behaviours, (ii) the extraction of associations between them and the local environment, (iii) the evaluation of an intervention in time, and (iv) the design of an action by predicting its effect. More than 3700 children, from more than 33 schools and 2 clinics, in 5 European cities have been monitored using a custom-made mobile application for the extraction of behavioural patterns through the capturing of accelerometer and geolocation data, while online databases were assessed in order to have a description of the environment. In this paper, OPdashboard functionality is described in detail, while the preliminary association outcomes in two European cities, namely Thessaloniki in Greece and Stockholm in Sweden, indicate a correlation between children’s eating and physical activity behaviours and the availability of food related places or sport facilities close to schools. In addition, OPdashboard was used to assess the modification of children’s physical activity as the result of the health policies applied for the deceleration of the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease on physical activity was statistically significant, while in the suburbs a slight increase was observed. Those findings suggest the importance of the open spaces availability on children’s behavioural change. However, additional factors must be taken into account in order to have a clearer understanding of the results. The OPdashboard is exposed as a web interface (http://bigo.med.auth.gr:3838/), while its functionality was evaluated during a focus group with experts on public health, where its usefulness on the better understanding of the interplay between children’s obesogenic behaviours and the environment was underlined.

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