Age, gender and season dependent 25(OH)D levels in children and adults living in Istanbul

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zeynep Yildiz ◽  
Özlem Hürmeydan ◽  
Özlem Çakır Madenci ◽  
Asuman Orçun ◽  
Nihal Yücel
Keyword(s):  
Large Scale ◽  
Population Characteristics ◽  
Healthcare Outcomes ◽  
Hydroxyvitamin D ◽  
Large Scale Data ◽  
Laboratory Results ◽  
Turkish People ◽  
Adults And Children ◽  
25 Hydroxyvitamin D ◽  
Scale Data

AbstractBackgroundWe evaluated population characteristics of serum 25-hydroxyvitamin D (25(OH)D) levels and determined the influence of age, gender and season in an extensive dataset.Materials and methodsLaboratory results of 103,509 adults and 19,186 children were retrospectively evaluated. Study group was classified regarding ages as; <40, 40–50, 50–60 and >60 years for adults and 0–1, 1–12 months,1–3, 4–6, 7–9, 10–12, 13–15 and 16–18 years for children. Seasonal values were also determined. Levels were measured by Architect i1000 SR (Abbott Diagnostics, USA).ResultsThe median (2.5–97.5 percentiles) of 25(OH)D levels were 38.75 (9.5–158.25) nmol/L for adults and 43.25 (11.25–125.5) nmol/L for children. There were significant gender differences for both adults and children. Values differed significantly among age subgroups (p’s < 0.01). A total of 63% of adults and 59.5% of children had 25(OH)D levels below 50 nmol/L (p < 0.001). 25(OH)D levels were significantly lower in the winter compared with summer (p’s < 0.001). Even levels in summer were moderate deficient for all group.ConclusionThe rate of 25(OH)D deficiency was remarkable during the whole year. This will provide large-scale data about 25(OH)D status in Turkish people and may contribute to the prevention and treatment of this condition for better healthcare outcomes.

scholarly journals Healthcare Analytics: A Comprehensive Review

2021 ◽  
Vol 11 (1) ◽  
pp. 6650-6655
Author(s):  
A. Alghamdi ◽  
T. Alsubait ◽  
A. Baz ◽  
H. Alhakami
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Predictive Analytics ◽  
Human Life ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Healthcare Outcomes ◽  
Large Scale Data ◽  
Scale Data

Big data have attracted significant attention in recent years, as their hidden potentials that can improve human life, especially when applied in healthcare. Big data is a reasonable collection of useful information allowing new breakthroughs or understandings. This paper reviews the use and effectiveness of data analytics in healthcare, examining secondary data sources such as books, journals, and other reputable publications between 2000 and 2020, utilizing a very strict strategy in keywords. Large scale data have been proven of great importance in healthcare, and therefore there is a need for advanced forms of data analytics, such as diagnostic data and descriptive analysis, for improving healthcare outcomes. The utilization of large-scale data can form the backbone of predictive analytics which is the baseline for future individual outcome prediction.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

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