scholarly journals Beyond simple charts: Design of visualizations for big health data

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Oluwakemi Ola ◽  
Kamran Sedig
Keyword(s):  
Big Data ◽  
Disease Surveillance ◽  
Large Scale ◽  
Health Management ◽  
High Volume ◽  
Health Data ◽  
Making Sense ◽  
Interactive Visualizations ◽  
Scatter Plots ◽  
At Risk Populations

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data’s utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data. 

scholarly journals BANKSAFE: Visual analytics for big data in large-scale computer networks

2013 ◽  
Vol 14 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Fabian Fischer ◽  
Johannes Fuchs ◽  
Florian Mansmann ◽  
Daniel A Keim
Keyword(s):  
Big Data ◽  
Computer Networks ◽  
Visual Analytics ◽  
Large Scale ◽  
Science And Technology ◽  
Lessons Learned ◽  
Unstructured Data ◽  
Monitoring Data ◽  
Making Sense

The enormous growth of data in the last decades led to a wide variety of different database technologies. Nowadays, we are capable of storing vast amounts of structured and unstructured data. To address the challenge of exploring and making sense out of big data using visual analytics, the tight integration of such backend services is needed. In this article, we introduce BANKSAFE, which was built for the VAST Challenge 2012 and won the outstanding comprehensive submission award. BANKSAFE is based on modern database technologies and is capable of visually analyzing vast amounts of monitoring data and security-related datasets of large-scale computer networks. To better describe and demonstrate the visualizations, we utilize the Visual Analytics Science and Technology (VAST) Challenge 2012 as case study. Additionally, we discuss lessons learned during the design and development of BANKSAFE, which are also applicable to other visual analytics applications for big data.

Challenges for Big Data Security and Privacy

2019 ◽  
pp. 57-66
Author(s):  
M. Govindarajan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Large Scale ◽  
High Volume ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Cloud Migration ◽  
Definitive Solution ◽  
Cloud Infrastructures ◽  
Privacy Issues

Security and privacy issues are magnified by the volume, variety, and velocity of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, big data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, big data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the big data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging big data security and privacy issues.

scholarly journals Methodological research priorities for data sciences: Report from The International Methodology Consortium for Coded Health Information (IMECCHI)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Rachel Jolley ◽  
Danielle Southern ◽  
Hude Quan ◽  
William Ghali ◽  
Bernard Burnand
Keyword(s):  
Big Data ◽  
Health Information ◽  
Disease Surveillance ◽  
Health Management ◽  
International Comparisons ◽  
Research Priorities ◽  
Clinical Decision ◽  
Methodological Research ◽  
Wide Range

ABSTRACT ObjectivesThe vast amount of data produced by healthcare systems both structured and unstructured, termed ‘Big Data’ have the potential to improve the quality of healthcare through supporting a wide range of medical and healthcare functions, including clinical decision support, disease surveillance, and population health management. As the field of big data in healthcare is rapidly expanding, methodology to understand and analyze thereby enhancing and optimizing the use of this data is needed. We present priorities determined for future work in this area. ApproachAn international collaboration of health services researchers who aim to promote the methodological development and use of coded health information to promote quality of care and quality health policy decisions known as IMECCHI –proposes areas of development and future priorities for use of big data in healthcare. Thematic areas were determined through discussion of potential projects related to the use and evaluation of both structured /codeable and unstructured health information, during a recent meeting in October 2015 ResultsSeveral themes were identified. The top priorities included: 1) electronic medical record data exploration and utilization; 2) developing common data models and multimodal /multi-source databases from disparate sources development; 3) data quality assessment including developing indicators, automated logic checks and international comparisons; 4) the translation of ICD-10 to ICD-11 through field-testing 5) Exploration of non-physician produced/coded data; and 6) Patient safety and quality measure development. ConclusionsA list of expert views on critical international priorities for future methodological research relating to big data in healthcare were determined. The consortium's members welcome contacts from investigators involved in research using health data, especially in cross-jurisdictional collaborative studies.

scholarly journals SARS-CoV-2 PCR and antibody testing for an entire rural community: methods and feasibility of high-throughput testing procedures

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Ayesha Appa ◽  
Gabriel Chamie ◽  
Aenor Sawyer ◽  
Kimberly Baltzell ◽  
Kathryn Dippell ◽  
...  
Keyword(s):  
Rural Community ◽  
Urban Areas ◽  
Disease Surveillance ◽  
Large Scale ◽  
High Volume ◽  
Medical Personnel ◽  
Blood Collection ◽  
Sample Collection ◽  
Antibody Testing ◽  
Testing Procedures

Abstract Background Early in the pandemic, inadequate SARS-CoV-2 testing limited understanding of transmission. Chief among barriers to large-scale testing was unknown feasibility, particularly in non-urban areas. Our objective was to report methods of high-volume, comprehensive SARS-CoV-2 testing, offering one model to augment disease surveillance in a rural community. Methods A community-university partnership created an operational site used to test most residents of Bolinas, California regardless of symptoms in 4 days (April 20th – April 23rd, 2020). Prior to testing, key preparatory elements included community mobilization, pre-registration, volunteer recruitment, and data management. On day of testing, participants were directed to a testing lane after site entry. An administrator viewed the lane-specific queue and pre-prepared test kits, linked to participants’ records. Medical personnel performed sample collection, which included finger prick with blood collection to run laboratory-based antibody testing and respiratory specimen collection for polymerase chain reaction (PCR). Results Using this 4-lane model, 1,840 participants were tested in 4 days. A median of 57 participants (IQR 47–67) were tested hourly. The fewest participants were tested on day 1 (n = 338 participants), an intentionally lower volume day, increasing to n = 571 participants on day 4. The number of testing teams was also increased to two per lane to allow simultaneous testing of multiple participants on days 2–4. Consistent staffing on all days helped optimize proficiency, and strong community partnership was essential from planning through execution. Conclusions High-volume ascertainment of SARS-CoV-2 prevalence by PCR and antibody testing was feasible when conducted in a community-led, drive-through model in a non-urban area.

Challenges for Big Data Security and Privacy

2018 ◽  
pp. 373-380
Author(s):  
M. Govindarajan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Large Scale ◽  
High Volume ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Cloud Migration ◽  
Definitive Solution ◽  
Cloud Infrastructures ◽  
Privacy Issues

Security and privacy issues are magnified by the volume, variety, and velocity of Big Data, such as Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, Big Data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, Big Data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the Big Data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging Big Data security and privacy issues.

scholarly journals A review of big data and medical research

2020 ◽  
Vol 8 ◽  
pp. 205031212093483 ◽  
Author(s):  
Mary Mallappallil ◽  
Jacob Sabu ◽  
Angelika Gruessner ◽  
Moro Salifu
Keyword(s):  
Big Data ◽  
Study Design ◽  
Large Scale ◽  
Health Data ◽  
Biological Data ◽  
Easy Access ◽  
Real World Data ◽  
Privacy And Security ◽  
Biometric Data ◽  
Electronic Health

Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. “Big data” is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data) and the relationships between industry, government regulators, and academics. Cultural changes along with easy access to information via the Internet facilitate ease of participation by more people. Current needs demand quick answers which may be supplied by big data, biobanks, and changes in flexibility in study design. Big data can reveal health patterns, and promises to provide solutions that have previously been out of society’s grasp; however, the murkiness of international laws, questions of data ownership, public ignorance, and privacy and security concerns are slowing down the progress that could otherwise be achieved by the use of big data. The goal of this descriptive review is to create awareness of the ramifications for big data and to encourage readers that this trend is positive and will likely lead to better clinical solutions, but, caution must be exercised to reduce harm.

scholarly journals A Computational Intelligence Approach to Predict Energy Demand Using Random Forest in a Cloudera Cluster

2021 ◽  
Vol 11 (18) ◽  
pp. 8635
Author(s):  
Laura Cáceres ◽  
Jose Ignacio Merino ◽  
Norberto Díaz-Díaz
Keyword(s):  
Big Data ◽  
Energy Demand ◽  
Large Scale ◽  
Demand Forecasting ◽  
Vertical Scaling ◽  
Business Decisions ◽  
Household Energy ◽  
Making Sense ◽  
Large Scale Data ◽  
Socioeconomic Data

Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for household energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.

On-farm udder health monitoring

2011 ◽  
Vol 39 (02) ◽  
pp. 95-100
Author(s):  
J. C. van Veersen ◽  
O. Sampimon ◽  
R. G. Olde Riekerink ◽  
T. J. G. Lam
Keyword(s):  
Management Practices ◽  
Health Management ◽  
Action Plan ◽  
Real Life ◽  
Health Data ◽  
Control Points ◽  
Udder Health ◽  
Critical Control Points ◽  
On Farm ◽  
Clinical Problems

SummaryIn this article an on-farm monitoring approach on udder health is presented. Monitoring of udder health consists of regular collection and analysis of data and of the regular evaluation of management practices. The ultimate goal is to manage critical control points in udder health management, such as hygiene, body condition, teat ends and treatments, in such a way that results (udder health parameters) are always optimal. Mastitis, however, is a multifactorial disease, and in real life it is not possible to fully prevent all mastitis problems. Therefore udder health data are also monitored with the goal to pick up deviations before they lead to (clinical) problems. By quantifying udder health data and management, a farm is approached as a business, with much attention for efficiency, thought over processes, clear agreements and goals, and including evaluation of processes and results. The whole approach starts with setting SMART (Specific, Measurable, Acceptable, Realistic, Time-bound) goals, followed by an action plan to realize these goals.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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