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

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(Updated article of the week text.)
(Updated article of the week text.)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Kalathil FrontInResMetAnal2018 2.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 BaldominosIntJOfIMAI2018 4-7.png|240px]]</div>
'''"[[Journal:Application of text analytics to extract and analyze material–application pairs from a large scientific corpus|Application of text analytics to extract and analyze material–application pairs from a large scientific corpus]]"'''
'''"[[Journal:DataCare: Big data analytics solution for intelligent healthcare management|DataCare: Big data analytics solution for intelligent healthcare management]]"'''


When assessing the importance of materials (or other components) to a given set of applications, machine analysis of a very large corpus of scientific abstracts can provide an analyst a base of insights to develop further. The use of text analytics reduces the time required to conduct an evaluation, while allowing analysts to experiment with a multitude of different hypotheses. Because the scope and quantity of [[metadata]] analyzed can, and should, be large, any divergence from what a human analyst determines and what the text analysis shows provides a prompt for the human analyst to reassess any preliminary findings. In this work, we have successfully extracted material–application pairs and ranked them on their importance. This method provides a novel way to map scientific advances in a particular material to the application for which it is used. ('''[[Journal:Application of text analytics to extract and analyze material–application pairs from a large scientific corpus|Full article...]]''')<br />
This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at rapid speeds from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction. ('''[[Journal:DataCare: Big data analytics solution for intelligent healthcare management|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
: ▪ [[Journal:Application of text analytics to extract and analyze material–application pairs from a large scientific corpus|Application of text analytics to extract and analyze material–application pairs from a large scientific corpus]]
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Revision as of 15:24, 24 July 2018

Fig1 BaldominosIntJOfIMAI2018 4-7.png

"DataCare: Big data analytics solution for intelligent healthcare management"

This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at rapid speeds from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction. (Full article...)

Recently featured:

Application of text analytics to extract and analyze material–application pairs from a large scientific corpus
Information management in context of scientific disciplines
A systematic framework for data management and integration in a continuous pharmaceutical manufacturing processing line