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1.
Rev Med Chil ; 149(2): 248-254, 2021 Feb.
Artículo en Español | MEDLINE | ID: mdl-34479270

RESUMEN

The processes associated with health care generate a large amount of information that is difficult to analyze using standard statistical procedures. In this context, disciplines such as Data Science became relevant, mainly through strategies such as Machine Learning (ML). The latter groups a series of tools whose purpose is to develop algorithms to extract information from data, whether for explanation, classification, or prediction. Despite its usefulness as support for clinical decisions, its potential in health care management has been less explored. Also, there are difficulties in understanding these types of studies. This work tries to offer a nontechnical overview of the ML concept and its advantages for health care management. It collects examples of ML applications in emergency department management.


Asunto(s)
Algoritmos , Aprendizaje Automático , Servicio de Urgencia en Hospital , Humanos
2.
Rev. méd. Chile ; 149(2): 248-254, feb. 2021. ilus
Artículo en Español | LILACS | ID: biblio-1389434

RESUMEN

The processes associated with health care generate a large amount of information that is difficult to analyze using standard statistical procedures. In this context, disciplines such as Data Science became relevant, mainly through strategies such as Machine Learning (ML). The latter groups a series of tools whose purpose is to develop algorithms to extract information from data, whether for explanation, classification, or prediction. Despite its usefulness as support for clinical decisions, its potential in health care management has been less explored. Also, there are difficulties in understanding these types of studies. This work tries to offer a nontechnical overview of the ML concept and its advantages for health care management. It collects examples of ML applications in emergency department management.


Asunto(s)
Humanos , Algoritmos , Aprendizaje Automático , Servicio de Urgencia en Hospital
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