Abstract LB-156: New-onset cancer cases in FDA’s Sentinel System: A large distributed system of US electronic healthcare data

2019 ◽  
Author(s):  
Nicole R. Haug ◽  
Anita K. Wagner ◽  
Katherine A. McGlynn ◽  
Charles E. Leonard ◽  
Michael D. Nguyen ◽  
...  
Keyword(s):  
Distributed System ◽  
Healthcare Data ◽  
System A ◽  
New Onset

Abstract LB-156: New-onset cancer cases in FDA’s Sentinel System: A large distributed system of US electronic healthcare data

2019 ◽  
Author(s):  
Nicole R. Haug ◽  
Anita K. Wagner ◽  
Katherine A. McGlynn ◽  
Charles E. Leonard ◽  
Michael D. Nguyen ◽  
...  
Keyword(s):  
Distributed System ◽  
Healthcare Data ◽  
System A ◽  
New Onset

scholarly journals The relationship between antihypertensive medications and mood disorders: analysis of linked healthcare data for 1.8 million patients

2020 ◽  
pp. 1-9 ◽  
Author(s):  
Richard J. Shaw ◽  
Daniel Mackay ◽  
Jill P. Pell ◽  
Sandosh Padmanabhan ◽  
David S. Bailey ◽  
...  
Keyword(s):  
Mood Disorder ◽  
Mood Disorders ◽  
Cox Regression ◽  
Previous History ◽  
Area Deprivation ◽  
Antihypertensive Medications ◽  
Healthcare Data ◽  
Increased Risk ◽  
History Of ◽  
New Onset

Abstract Background Recent work suggests that antihypertensive medications may be useful as repurposed treatments for mood disorders. Using large-scale linked healthcare data we investigated whether certain classes of antihypertensive, such as angiotensin antagonists (AAs) and calcium channel blockers, were associated with reduced risk of new-onset major depressive disorder (MDD) or bipolar disorder (BD). Method Two cohorts of patients treated with antihypertensives were identified from Scottish prescribing (2009–2016) and hospital admission (1981–2016) records. Eligibility for cohort membership was determined by a receipt of a minimum of four prescriptions for antihypertensives within a 12-month window. One treatment cohort (n = 538 730) included patients with no previous history of mood disorder, whereas the other (n = 262 278) included those who did. Both cohorts were matched by age, sex and area deprivation to untreated comparators. Associations between antihypertensive treatment and new-onset MDD or bipolar episodes were investigated using Cox regression. Results For patients without a history of mood disorder, antihypertensives were associated with increased risk of new-onset MDD. For AA monotherapy, the hazard ratio (HR) for new-onset MDD was 1.17 (95% CI 1.04–1.31). Beta blockers' association was stronger (HR 2.68; 95% CI 2.45–2.92), possibly indicating pre-existing anxiety. Some classes of antihypertensive were associated with protection against BD, particularly AAs (HR 0.46; 95% CI 0.30–0.70). For patients with a past history of mood disorders, all classes of antihypertensives were associated with increased risk of future episodes of MDD. Conclusions There was no evidence that antihypertensive medications prevented new episodes of MDD but AAs may represent a novel treatment avenue for BD.

scholarly journals LHCb Continuous Integration and Deployment system: a message based approach

2019 ◽  
Vol 214 ◽  
pp. 05001 ◽  
Author(s):  
Stefan-Gabriel Chitic ◽  
Ben Couturier ◽  
Marco Clemencic ◽  
Joel Closier
Keyword(s):  
Distributed System ◽  
File System ◽  
Source Code ◽  
Priority Queues ◽  
Integration System ◽  
Continuous Integration ◽  
System A ◽  
Event Based ◽  
New System

A continuous integration system is crucial to maintain the quality of the 6 millions lines of C++ and Python source code of the LHCb software in order to ensure consistent builds of the software as well as to run the unit and integration tests. Jenkins automation server is used for this purpose. It builds and tests around 100 configurations and produces in the order of 1500 built artifacts per day which are installed on the CVMFS file system or potentially on the developers’ machines. Faced with a large and growing number of configurations built every day, and in order to ease inter-operation between the continuous integration system and the developers, we decided to put in place a flexible messaging system. As soon as the built artifacts have been produced, the distributed system allows their deployment based on the priority of the configurations. We will describe the architecture of the new system, which is based on RabbitMQ messaging system (and the pika Python client library), and uses priority queues to start the LHCb software integration tests and to drive the installation of the nightly builds on the CVMFS file system. We will also show how the introduction of an event based system can help with the communication of results to developers.

Importance of Big Data In Healthcare System A Survey Approach

2020 ◽  
pp. 47-56
Author(s):  
M. Pavithra ◽  
E. S. Shamila ◽  
G. Krishna Priya ◽  
G. VijiPriya ◽  
R. Ashwini
Keyword(s):  
Big Data ◽  
Healthcare Providers ◽  
Public Healthcare ◽  
Healthcare Industry ◽  
Healthcare Organizations ◽  
Public And Private ◽  
Healthcare Data ◽  
System A ◽  
Efficient Management ◽  
Public And Private Sector

<p>‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, and results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.</p>

scholarly journals Optimal Data File Allocation for All-to-All Comparison in Distributed System: A Case Study on Genetic Sequence Comparison

2019 ◽  
Vol 14 (2) ◽  
pp. 199-211 ◽  
Author(s):  
Leixiao Li ◽  
Jing Gao ◽  
Ren Mu
Keyword(s):  
Distributed System ◽  
Large Scale ◽  
Data File ◽  
Mixed Integer ◽  
Data Allocation ◽  
Allocation Model ◽  
Large Scale Data ◽  
System A ◽  
File Allocation ◽  
Unbalanced Load

In order to solve the problem of unbalanced load of data les in large-scale data all-to-all comparison under distributed system environment, the differences of les themselves arefully considered. This paper aims to fully utilize the advantages of distributed system to enhance the le allocation of all-to-all comparison between the data les in a large dataset. For this purpose, the author formally described the all-to-all comparison problem, and con-structed a data allocation model via mixed integer linear programming (MILP). Meanwhile, a data allocation algorithm was developed on the Matlab using the intlinprog function of branch-and-bound method. Finally, our model and algorithm were veried through several experiments. The results show that the proposed le allocation strategy can achieve the basic load balance of each node in the distributed system without exceeding the storage capacity of any node, and completely localize the data le. The research ndings can be applied to such elds as bioinformatics, biometrics and data mining.

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