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big data analytics

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
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
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
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
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
Bernd Mayer, Andreas Heinzel, Arno Lukas, Paul Perco
BACKGROUND: Productivity in drug R&D continues seeing significant attrition in clinical stage testing. Approval of new molecular entities proceeds with slow pace specifically when it comes to chronic, age-related diseases, calling for new conceptual approaches, methodological implementation and organizational adoption in drug development. METHODS: Detailed phenotyping of disease presentation together with comprehensive representation of drug mechanism of action is considered as a path forward, and a big data spectrum has become available covering behavioral, clinical and molecular characteristics, the latter combining reductionist and explorative strategies...
October 6, 2016: Current Pharmaceutical Design
Peter V Coveney, Edward R Dougherty, Roger R Highfield
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales...
November 13, 2016: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Chun Wang, Ming-Hui Chen, Elizabeth Schifano, Jing Wu, Jun Yan
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data...
2016: Statistics and its Interface
Pavani Yashodha De Silva, Gamage Upeksha Ganegoda
With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past...
2016: BioMed Research International
Dehui Luo, Xiang Wan, Jiming Liu, Tiejun Tong
The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results...
September 27, 2016: Statistical Methods in Medical Research
Costanza Cucci, John K Delaney, Marcello Picollo
Diffuse reflectance hyperspectral imaging, or reflectance imaging spectroscopy, is a sophisticated technique that enables the capture of hundreds of images in contiguous narrow spectral bands (bandwidth < 10 nm), typically in the visible (Vis, 400-750 nm) and the near-infrared (NIR, 750-2500 nm) regions. This sequence of images provides a data set that is called an image-cube or file-cube. Two dimensions of the image-cube are the spatial dimensions of the scene, and the third dimension is the wavelength...
October 18, 2016: Accounts of Chemical Research
Sergei V Kalinin, Evgheni Strelcov, Alex Belianinov, Suhas Somnath, Rama K Vasudevan, Eric J Lingerfelt, Richard K Archibald, Chaomei Chen, Roger Proksch, Nouamane Laanait, Stephen Jesse
Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies...
September 27, 2016: ACS Nano
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
Steven Thompson, Stephen Varvel, Maciek Sasinowski, James P Burke
Big data and advances in analytical processes represent an opportunity for the healthcare industry to make better evidence-based decisions on the value generated by various tests, procedures, and interventions. Value-based reimbursement is the process of identifying and compensating healthcare providers based on whether their services improve quality of care without increasing cost of care or maintain quality of care while decreasing costs. In this article, we motivate and illustrate the potential opportunities for payers and providers to collaborate and evaluate the clinical and economic efficacy of different healthcare services...
September 2016: Big Data
Colin Depp, John Torous, Wesley Thompson
Recognition and timely action around "warning signs" of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients...
September 7, 2016: JMIR Mental Health
Demissie Alemayehu, Marc L Berger
The explosion of data sources, accompanied by the evolution of technology and analytical techniques, has created considerable challenges and opportunities for drug development and healthcare resource utilization. We present a systematic overview these phenomena, and suggest measures to be taken for effective integration of the new developments in the traditional medical research paradigm and health policy decision making. Special attention is paid to pertinent issues in emerging areas, including rare disease drug development, personalized medicine, Comparative Effectiveness Research, and privacy and confidentiality concerns...
2016: Health Services & Outcomes Research Methodology
Qi Liu, Weidong Cai, Dandan Jin, Jian Shen, Zhangjie Fu, Xiaodong Liu, Nigel Linge
Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems...
2016: Sensors
Matthew R Cribbet, Ryan W Logan, Mathew D Edwards, Erin Hanlon, Clara Bien Peek, Jeremy J Stubblefield, Sridhar Vasudevan, Fiona Ritchey, Ellen Frank
This paper focuses on the relationship between the circadian system and glucose metabolism. Research across the translational spectrum confirms the importance of the circadian system for glucose metabolism and offers promising clues as to when and why these systems go awry. In particular, basic research has started to clarify the molecular and genetic mechanisms through which the circadian system regulates metabolism. The study of human behavior, especially in the context of psychiatric disorders, such as bipolar disorder and major depression, forces us to see how inextricably linked mental health and metabolic health are...
September 2, 2016: Annals of the New York Academy of Sciences
Issam El Naqa
Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks...
August 29, 2016: Methods: a Companion to Methods in Enzymology
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