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Big data

Adam R Smith, Melissa R Proffitt, Winnie W Ho, Claire B Mullaney, Javier A Maldonado-Ocampo, Nathan R Lovejoy, José A Alves-Gomes, G Troy Smith
The electric communication signals of weakly electric ghost knifefishes (Gymnotiformes: Apteronotidae) provide a valuable model system for understanding the evolution and physiology of behavior. Apteronotids produce continuous wave-type electric organ discharges (EODs) that are used for electrolocation and communication. The frequency and waveform of EODs, as well as the structure of transient EOD modulations (chirps), vary substantially across species. Understanding how these signals have evolved, however, has been hampered by the lack of a well-supported phylogeny for this family...
October 18, 2016: Journal of Physiology, Paris
Ming-Ching Shen, Ming Chen, Gwo-Chin Ma, Shun-Ping Chang, Ching-Yeh Lin, Bo-Do Lin, Han-Ni Hsieh
BACKGROUND: Von Willebrand disease (VWD) is not uncommon in Taiwan. In type 2 or type 3 VWD hemorrhagic symptoms are severer and laboratory data relatively more distinctive. De novo mutation and somatic mosaicism of type 2 VWD gene were rarely reported. Therefore clinical, laboratory and genetic studies of only type 2A, 2B and 2M VWD will be presented and issues of de novo mutation and somatic mosaicism will be explored. METHODS: Fifty-four patients belonging to 23 unrelated families from all around the country in whom type 2 VWD exclusive of type 2N has been diagnosed not only by clinical and routine laboratory studies but also by genetic confirmation during 1990-2015 were investigated...
2016: Thrombosis Journal
Alistair E W Johnson, Mohammad M Ghassemi, Shamim Nemati, Katherine E Niehaus, David A Clifton, Gari D Clifford
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms...
February 2016: Proceedings of the IEEE
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
Hideki Kato, Keita Sakai, Mizuki Uchiyama, Kentaro Suzuki
The diagnostic reference levels (DRLs) of the general X-ray radiography are defined by the absorbed dose of air at the entrance surface with backscattered radiation from a scattering medium. Generally, the entrance surface dose of the general X-ray radiography is calculated from measured air kerma of primary X-ray multiplied by a backscatter factor (BSF). However, the BSF data employed at present used water for scattering medium, and was calculated based on the water-absorbed dose by incident primary photons and backscattered photons from the scattering medium...
2016: Nihon Hoshasen Gijutsu Gakkai Zasshi
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
Jennifer M Hensel, Jay Shaw, Lianne Jeffs, Noah M Ivers, Laura Desveaux, Ashley Cohen, Payal Agarwal, Walter P Wodchis, Joshua Tepper, Darren Larsen, Anita McGahan, Peter Cram, Geetha Mukerji, Muhammad Mamdani, Rebecca Yang, Ivy Wong, Nike Onabajo, Trevor Jamieson, R Sacha Bhatia
BACKGROUND: Mental illness is a substantial and rising contributor to the global burden of disease. Access to and utilization of mental health care, however, is limited by structural barriers such as specialist availability, time, out-of-pocket costs, and attitudinal barriers including stigma. Innovative solutions like virtual care are rapidly entering the health care domain. The advancement and adoption of virtual care for mental health, however, often occurs in the absence of rigorous evaluation and adequate planning for sustainability and spread...
October 18, 2016: BMC Psychiatry
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
Peng Jiang, Zhixin Hu, Jun Liu, Shanen Yu, Feng Wu
Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process...
October 13, 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
Marko Poglitsch
Primary aldosteronism (PA) is severe form of hypertension characterized by a strongly increased aldosterone secretion mediated by adenomas or other forms of adrenal hyper-activity. Once detected, PA can be usually cured by either surgical intervention or by appropriate pharmacologic treatments. The incidence of PA among hypertensive patients varies strongly between different studies, which is in part caused by the complex state-of-the-art testing procedure that is unfortunately far away from being a versatile PA screening tool...
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
Anna Dominiczak
Human primary or essential hypertension is a complex, polygenic trait with some 50% contribution from genes and environment. Richard Lifton and colleagues provided elegant dissection of several rare Mendelian forms of hypertension, exemplified by the glucocorticoid remediable aldosteronism and Liddle's syndrome. These discoveries illustrate that a single gene mutation can explain the entire pathogenesis of severe, early onset hypertension as well as dictating the best treatment.The dissection of the much more common polygenic hypertension has proven much more difficult...
September 2016: Journal of Hypertension
Iwao Kuwajima
Since a concept of Evidence-based Medicine appeared in medical field in 1991, modern medical treatment have been remarkably changed.However, delusive belief of EBM without criticism has brought negative aspect, such as utilization of EBM by companies as a tool of promotion of drug or medical device.Although most of clinical trials were financially supported by drug companies. result of clinical trial does not always ended in favor of test drug or device. When negative results appeared, various way were taken by industry such as usage of SPIN, emphasizing secondary endpoint...
September 2016: Journal of Hypertension
William Wijns, Emanuele Barbato
No abstract text is available yet for this article.
October 20, 2016: EuroIntervention
Christian Güldner, Isabell Diogo, Eva Bernd, Stephanie Dräger, Magis Mandapathil, Afshin Teymoortash, Hesham Negm, Thomas Wilhelm
Cone beam computed tomography (CBCT, syn. digital volume tomography = DVT) was introduced into ENT imaging more than 10 years ago. The main focus was on imaging of the paranasal sinuses and traumatology of the mid face. In recent years, it has also been used in imaging of chronic ear diseases (especially in visualizing middle and inner ear implants), but an exact description of the advantages and limitations of visualizing precise anatomy in a relevant number of patients is still missing. The data sets of CBCT imaging of the middle and inner ear of 204 patients were analyzed regarding the visualization of 18 different anatomic structures...
October 17, 2016: European Archives of Oto-rhino-laryngology
Jake Luo, Christina Eldredge, Chi C Cho, Ron A Cisler
BACKGROUND: Understanding adverse event patterns in clinical studies across populations is important for patient safety and protection in clinical trials as well as for developing appropriate drug therapies, procedures, and treatment plans. OBJECTIVES: The objective of our study was to conduct a data-driven population-based analysis to estimate the incidence, diversity, and association patterns of adverse events by age of the clinical trials patients and participants...
October 17, 2016: JMIR Medical Informatics
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