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https://www.readbyqxmd.com/read/28109929/paradigm-shift-in-medical-data-management-big-data-and-small-data
#1
EDITORIAL
James B Seward
No abstract text is available yet for this article.
January 12, 2017: JACC. Cardiovascular Imaging
https://www.readbyqxmd.com/read/28109319/for-robust-big-data-analyses-a-collection-of-150-important-pro-metastatic-genes
#2
REVIEW
Yan Mei, Jun-Ping Yang, Chao-Nan Qian
Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses...
January 21, 2017: Chinese Journal of Cancer
https://www.readbyqxmd.com/read/28108892/the%C3%A2-premise-and-promise-of-big-data-for-tracking-population-health-big-deal-or-big-disappointment
#3
EDITORIAL
Emad Mansoor, Sadeer G Al-Kindi
No abstract text is available yet for this article.
January 20, 2017: Digestive Diseases and Sciences
https://www.readbyqxmd.com/read/28106594/big-data-and-nursing-implications-for-the-future
#4
Maxim Topaz, Lisiane Pruinelli
Big data is becoming increasingly more prevalent and it affects the way nurses learn, practice, conduct research and develop policy. The discipline of nursing needs to maximize the benefits of big data to advance the vision of promoting human health and wellbeing. However, current practicing nurses, educators and nurse scientists often lack the required skills and competencies necessary for meaningful use of big data. Some of the key skills for further development include the ability to mine narrative and structured data for new care or outcome patterns, effective data visualization techniques, and further integration of nursing sensitive data into artificial intelligence systems for better clinical decision support...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28106575/introduction-forecasting-informatics-competencies-for-nurses-in-the-future-of-connected-health
#5
Judy Murphy, William Goossen
This introduction to the book discusses how the topic of competencies for nurses in a world of connected health needs to be addressed at the curriculum level to achieve the specific competencies for various roles, including practicing nurse, nurse teacher, nurse leader, and nursing informatics specialists. It looks back at milestone publications from the international Nursing Informatics post conferences that still serve a purpose for inspiring developments today and looks forward to the way nurses can use connected health to improve the health and health care for their patients...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28104854/big-data-approaches-to-protein-structure-prediction
#6
Johannes Söding
No abstract text is available yet for this article.
January 20, 2017: Science
https://www.readbyqxmd.com/read/28098389/rosacea-inflammatory-bowel-disease-and-the-value-of-big-data-and-of-epidemiological-studies
#7
C R Meier
No abstract text is available yet for this article.
January 2017: British Journal of Dermatology
https://www.readbyqxmd.com/read/28097074/resources-available-for-autism-research-in-the-big-data-era-a-systematic-review
#8
Reem Al-Jawahiri, Elizabeth Milne
Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research...
2017: PeerJ
https://www.readbyqxmd.com/read/28094538/the-obesity-paradox-in-colorectal-cancer-surgery-an-analysis-of-korean-healthcare-big-data-2012-2013
#9
Sanghun Lee
Although it is well known that obesity increases the risk of colorectal cancer, several studies have recently suggested that those who are overweight or class-one obese have better outcomes after surgery. However, the impact of obesity on the success of colorectal cancer surgery remains controversial. The medical records of patients diagnosed with colorectal cancer who were treated surgically from 2012 through 2013 were retrospectively analyzed. Data from a total of 36,740 patients were provided by the Healthcare Big Data Hub of the Korean Health Insurance Review & Assessment Service...
January 17, 2017: Nutrition and Cancer
https://www.readbyqxmd.com/read/28093877/a-shovel-ready-solution-to-fill-the-nursing-data-gap-in-the-interdisciplinary-clinical-picture
#10
Gail M Keenan, Karen Dunn Lopez, Vanessa E C Sousa, Janet Stifter, Tamara G R Macieira, Andrew D Boyd, Yingwei Yao, T Heather Herdman, Sue Moorhead, Anna McDaniel, Diana J Wilkie
PURPOSE: To critically evaluate 2014 American Academy of Nursing (AAN) call-to-action plan for generating interoperable nursing data. DATA SOURCES: Healthcare literature. DATA SYNTHESIS: AAN's plan will not generate the nursing data needed to participate in big data science initiatives in the short term because Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine - Clinical Terms are not yet ripe for generating interoperable data...
January 16, 2017: International Journal of Nursing Knowledge
https://www.readbyqxmd.com/read/28093689/characterizing-a-big-data-cohort-of-over-200-000-low-income-u-s-infants-and-children-for-obesity-research-the-advance-early-life-cohort
#11
J Boone-Heinonen, C J Tillotson, J P O'Malley, E K Cottrell, J A Gaudino, A Amofah, M L Rivo, A Brickman, K Mayer, M A McBurnie, R Gold, J E DeVoe
Introduction Low-income populations have elevated exposure to early life risk factors for obesity, but are understudied in longitudinal research. Our objective was to assess the utility of a cohort derived from electronic health record data from safety net clinics for investigation of obesity emerging in early life. Methods We examined data from the PCORNet ADVANCE Clinical Data Research Network, a national network of Federally-Qualified Health Centers serving >1.7 million safety net patients across the US...
January 16, 2017: Maternal and Child Health Journal
https://www.readbyqxmd.com/read/28092796/global-open-data-management-in-metabolomics
#12
REVIEW
Kenneth Haug, Reza M Salek, Christoph Steinbeck
Chemical Biology employs chemical synthesis, analytical chemistry and other tools to study biological systems. Recent advances in both molecular biology such as next generation sequencing (NGS) have led to unprecedented insights towards the evolution of organisms' biochemical repertoires. Because of the specific data sharing culture in Genomics, genomes from all kingdoms of life become readily available for further analysis by other researchers. While the genome expresses the potential of an organism to adapt to external influences, the Metabolome presents a molecular phenotype that allows us to asses the external influences under which an organism exists and develops in a dynamic way...
January 13, 2017: Current Opinion in Chemical Biology
https://www.readbyqxmd.com/read/28092549/learning-short-binary-codes-for-large-scale-image-retrieval
#13
Li Liu, Mengyang Yu, Ling Shao
Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/ binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092471/genomics-pipelines-and-data-integration-challenges-and-opportunities-in-the-research-setting
#14
Jeremy Davis-Turak, Sean M Courtney, E Starr Hazard, W Bailey Glen, Willian da Silveira, Timothy Wesselman, Larry P Harbin, Bethany J Wolf, Dongjun Chung, Gary Hardiman
The emergence and mass utilization of high-throughput (HT) technologies, including sequencing technologies (genomics) and mass spectrometry (proteomics, metabolomics, lipids), has allowed geneticists, biologists, and biostatisticians to bridge the gap between genotype and phenotype on a massive scale. These new technologies have brought rapid advances in our understanding of cell biology, evolutionary history, microbial environments, and are increasingly providing new insights and applications towards clinical care and personalized medicine...
January 16, 2017: Expert Review of Molecular Diagnostics
https://www.readbyqxmd.com/read/28090521/big-data-to-the-rescue-of-systemic-inflammatory-response-syndrome-is-electronic-data-mining-the-way-of-the-future
#15
EDITORIAL
Utsav Nandi, Michael A Puskarich, Alan E Jones
No abstract text is available yet for this article.
December 2016: Annals of Translational Medicine
https://www.readbyqxmd.com/read/28079771/advanced-research-and-data-methods-in-women-s-health-big-data-analytics-adaptive-studies-and-the-road-ahead
#16
Christian R Macedonia, Clark T Johnson, Indika Rajapakse
Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. "Big data" is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies...
January 9, 2017: Obstetrics and Gynecology
https://www.readbyqxmd.com/read/28079723/toward-automating-hiv-identification-machine-learning-for-rapid-identification-of-hiv-related-social-media-data
#17
Sean D Young, Wenchao Yu, Wei Wang
INTRODUCTION: "Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data...
February 1, 2017: Journal of Acquired Immune Deficiency Syndromes: JAIDS
https://www.readbyqxmd.com/read/28075347/sdtcp-towards-datacenter-tcp-congestion-control-with-sdn-for-iot-applications
#18
Yifei Lu, Zhen Ling, Shuhong Zhu, Ling Tang
The Internet of Things (IoT) has gained popularity in recent years. Today's IoT applications are now increasingly deployed in cloud platforms to perform Big Data analytics. In cloud data center networks (DCN), TCP incast usually happens when multiple senders simultaneously communicate with a single receiver. However, when TCP incast happens, DCN may suffer from both throughput collapse for TCP burst flows and temporary starvation for TCP background flows. In this paper, we propose a software defined network (SDN)-based TCP congestion control mechanism, referred to as SDTCP, to leverage the features, e...
January 8, 2017: Sensors
https://www.readbyqxmd.com/read/28072379/the-challenge-of-in-vivo-tissue-characterization-connectivity-and-big-data
#19
EDITORIAL
C Gandini Wheeler-Kingshott
No abstract text is available yet for this article.
October 2016: Functional Neurology
https://www.readbyqxmd.com/read/28070484/deep-learning-predictions-of-survival-based-on-mri-in-amyotrophic-lateral-sclerosis
#20
Hannelore K van der Burgh, Ruben Schmidt, Henk-Jan Westeneng, Marcel A de Reus, Leonard H van den Berg, Martijn P van den Heuvel
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic...
2017: NeuroImage: Clinical
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