Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patte...

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Autores principales: Balladini, Javier, Rozas, Claudia, Frati, Fernando Emmanuel, Vicente, Néstor, Orlandi, Cristina
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Ciencias Informáticas
Cuidados Intensivos
Real time
big data
cloud computing
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/50088
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST41-Paper-3.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-50088
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Cuidados Intensivos
Real time
big data
cloud computing
spellingShingle Ciencias Informáticas
Cuidados Intensivos
Real time
big data
cloud computing
Balladini, Javier
Rozas, Claudia
Frati, Fernando Emmanuel
Vicente, Néstor
Orlandi, Cristina
Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
topic_facet Ciencias Informáticas
Cuidados Intensivos
Real time
big data
cloud computing
description In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.
format Articulo
Articulo
author Balladini, Javier
Rozas, Claudia
Frati, Fernando Emmanuel
Vicente, Néstor
Orlandi, Cristina
author_facet Balladini, Javier
Rozas, Claudia
Frati, Fernando Emmanuel
Vicente, Néstor
Orlandi, Cristina
author_sort Balladini, Javier
title Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
title_short Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
title_full Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
title_fullStr Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
title_full_unstemmed Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital
title_sort big data analytics in intensive care units: challenges and applicability in an argentinian hospital
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/50088
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST41-Paper-3.pdf
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AT vicentenestor bigdataanalyticsinintensivecareunitschallengesandapplicabilityinanargentinianhospital
AT orlandicristina bigdataanalyticsinintensivecareunitschallengesandapplicabilityinanargentinianhospital
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