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"Big data"

Ho Ting Wong, Vico Chung Lim Chiang, Kup Sze Choi, Alice Yuen Loke
The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size...
October 17, 2016: International Journal of Environmental Research and Public Health
S M Reza Soroushmehr, Kayvan Najarian
Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data...
September 2016: Dialogues in Clinical Neuroscience
Vicki Hertzberg, Valerie Mac, Lisa Elon, Nathan Mutic, Abby Mutic, Katherine Peterman, J Antonio Tovar-Aguilar, Eugenia Economos, Joan Flocks, Linda McCauley
Affordable measurement of core body temperature (Tc) in a continuous, real-time fashion is now possible. With this advance comes a new data analysis paradigm for occupational epidemiology. We characterize issues arising after obtaining Tc data over 188 workdays for 83 participating farmworkers, a population vulnerable to effects of rising temperatures due to climate change. We describe a novel approach to these data using smoothing and functional data analysis. This approach highlights different data aspects compared with describing Tc at a single time point or summaries of the time course into an indicator function (e...
October 18, 2016: Western Journal of Nursing Research
Ashfaq Khokhar, Muhammad Kamran Lodhi, Yingwei Yao, Rashid Ansari, Gail Keenan, Diana J Wilkie
Despite an unprecedented amount of health-related data being amassed from various technological innovations, our ability to process this data and extract hidden knowledge has yet to catch up with this explosive growth. Although nursing care plans can be an effective tool to support the achievement of desired patient outcomes, their online collection, storage, and processing is lagging far behind. As a result, the impact of nursing care is not well understood from qualitative as well as quantitative perspectives...
October 18, 2016: Western Journal of Nursing Research
Hao Chen, Xiaoyun Xie, Wanneng Shu, Naixue Xiong
With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations...
October 15, 2016: Sensors
Peter Rijnbeek
Massive numbers of electronic health records are currently being collected globally, including structured data in the form of diagnoses, medications, laboratory test results, and unstructured data contained in clinical narratives. This opens unprecedented possibilities for research and ultimately patient care. However, actual use of these databases in a multi-center study is severely hampered by a variety of challenges, e.g., each database has a different database structure and uses different terminology systems...
September 2016: Journal of Hypertension
Rae Woong Park
Big data indicates the large and ever-increasing volumes of data adhere to the following 4Vs: volume (ever-increasing amount), velocity (quickly generated), variety (many different types), veracity (from trustable sources). The last decade has seen huge advances in the amount of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. The routine operation of modern health care systems also produces an abundance of electronically stored data on an ongoing basis as a byproduct of clinical practice...
September 2016: Journal of Hypertension
Ki Chul Sung
Metabolic syndrome (MetS) is a clustering of cardiometabolic risk factors linked to insulin resistance and visceral obesity. Since 1988, when Gerald Reaven first described MetS as "Syndrome X," an abundance of research has been undertaken on its pathophysiology, prognosis, implications, therapeutic strategies, and clinical relationships with other metabolic diseases. Experts have focused on MetS during the last few decades not only because of the increasing importance of obesity in the development of metabolic diseases but also because of the effect of MetS on mortality and the development of cardiovascular diseases...
September 2016: Journal of Hypertension
William Wijns, Emanuele Barbato
No abstract text is available yet for this article.
October 20, 2016: EuroIntervention
Yehoshua Perl, James Geller, Michael Halper, Christopher Ochs, Ling Zheng, Joan Kapusnik-Uner
The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited. We describe summarization and visualization techniques that capture the big picture of a BK knowledgebase, possibly created from Big Data...
October 17, 2016: Annals of the New York Academy of Sciences
Andreas Holzinger
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples...
June 2016: Brain Informatics
Milad Makkie, Shijie Zhao, Xi Jiang, Jinglei Lv, Yu Zhao, Bao Ge, Xiang Li, Junwei Han, Tianming Liu
Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed...
December 2015: Brain Informatics
Xingjian Xu, Zhaohua Ji, Zhang Zhang
: Phylogeny reconstruction is fundamentally crucial for molecular evolutionary studies but remains computationally challenging. Here we present CloudPhylo, a tool built on Spark that is capable of processing large-scale datasets for phylogeny reconstruction. As testified on empirical data, CloudPhylo is well suited for big data analysis, achieving high efficiency and good scalability on phylogenetic tree inference. AVAILABILITY: CONTACT: zhangzhang@big...
October 14, 2016: Bioinformatics
Aikaterini Kotrotsou, Pascal O Zinn, Rivka R Colen
The role of radiomics in the diagnosis, monitoring, and therapy planning of brain tumors is becoming increasingly clear. Incorporation of quantitative approaches in radiology, in combination with increased computer power, offers unique insights into macroscopic tumor characteristics and their direct association with the underlying pathophysiology. This article presents the most recent findings in radiomics and radiogenomics with respect to identifying potential imaging biomarkers with prognostic value that can lead to individualized therapy...
November 2016: Magnetic Resonance Imaging Clinics of North America
Shakoor Hajat, Ceri Whitmore, Christophe Sarran, Andy Haines, Brian Golding, Harriet Gordon-Brown, Anthony Kessel, Lora E Fleming
BACKGROUND: Improved data linkages between diverse environment and health datasets have the potential to provide new insights into the health impacts of environmental exposures, including complex climate change processes. Initiatives that link and explore big data in the environment and health arenas are now being established. OBJECTIVES: To encourage advances in this nascent field, this article documents the development of a web browser application to facilitate such future research, the challenges encountered to date, and how they were addressed...
October 11, 2016: Science of the Total Environment
Priyakshi Kalita-de Croft, Fares Al-Ejeh, Amy E McCart Reed, Jodi M Saunus, Sunil R Lakhani
Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care...
November 2016: Advances in Anatomic Pathology
Diane J Skiba
No abstract text is available yet for this article.
September 2016: Nursing Education Perspectives
Po-Yen Wu, Chih-Wen Cheng, Chanchala Kaddi, Janani Venugopalan, Ryan Hoffman, May D Wang
OBJECTIVE: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. METHODS: In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling...
October 10, 2016: IEEE Transactions on Bio-medical Engineering
Denzil G Fiebig
Within a generation, empirical researchers have experienced unprecedented increases in the availability of data. 'Big data' has arrived with considerable hype and a sense that these are dramatic shifts in the research environment that have wide-reaching implications across many disciplines. There is no doubt that the analysis of new and varied sources of data currently available to researchers in health have the potential to better measure, monitor and describe health outcomes of patients and to uncover interesting patterns in how patients respond to treatments and interact with the health system...
October 13, 2016: Patient
Tsuyoshi Hamada, NaNa Keum, Reiko Nishihara, Shuji Ogino
Molecular pathological epidemiology (MPE) is an integrative field that utilizes molecular pathology to incorporate interpersonal heterogeneity of a disease process into epidemiology. In each individual, the development and progression of a disease are determined by a unique combination of exogenous and endogenous factors, resulting in different molecular and pathological subtypes of the disease. Based on "the unique disease principle," the primary aim of MPE is to uncover an interactive relationship between a specific environmental exposure and disease subtypes in determining disease incidence and mortality...
October 13, 2016: Journal of Gastroenterology
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