Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text.)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Elaboudi AdvInBioinfo2018 2018.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Stura EpidemBiostatPubHealth2018 15-2.png|240px]]</div>
'''"[[Journal:Big data management for healthcare systems: Architecture, requirements, and implementation|Big data management for healthcare systems: Architecture, requirements, and implementation]]"'''
'''"[[Journal:A new numerical method for processing longitudinal data: Clinical applications|A new numerical method for processing longitudinal data: Clinical applications]]"'''


The growing amount of data in the healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing [[Information management|data management]] architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on Spark and MongoDB tools. ('''[[Journal:Big data management for healthcare systems: Architecture, requirements, and implementation|Full article...]]''')<br />
Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control, and weather forecasting. Given some longitudinal data, i.e., scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Here, we propose an alternative approach to be used as an effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. ('''[[Journal:A new numerical method for processing longitudinal data: Clinical applications|Full article...]]''')<br />
<br />
<br />
''Recently featured'':
''Recently featured'':
: ▪ [[Journal:Big data management for healthcare systems: Architecture, requirements, and implementation|Big data management for healthcare systems: Architecture, requirements, and implementation]]
: ▪ [[Journal:Support Your Data: A research data management guide for researchers|Support Your Data: A research data management guide for researchers]]
: ▪ [[Journal:Support Your Data: A research data management guide for researchers|Support Your Data: A research data management guide for researchers]]
: ▪ [[Journal:CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network|CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network]]
: ▪ [[Journal:CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network|CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network]]
: ▪ [[Journal:How could the ethical management of health data in the medical field inform police use of DNA?|How could the ethical management of health data in the medical field inform police use of DNA?]]

Revision as of 18:06, 10 December 2018

Fig1 Stura EpidemBiostatPubHealth2018 15-2.png

"A new numerical method for processing longitudinal data: Clinical applications"

Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control, and weather forecasting. Given some longitudinal data, i.e., scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Here, we propose an alternative approach to be used as an effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. (Full article...)

Recently featured:

▪ Big data management for healthcare systems: Architecture, requirements, and implementation
▪ Support Your Data: A research data management guide for researchers
▪ CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network
Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=34412"