Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31357

TitleReal-time data mining models for predicting length of stay in intensive care units
Author(s)Veloso, Rui
Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
KeywordsLength of stay
INTCare
Intensive care units
Data mining
Real-time
Issue dateNov-2014
PublisherINSTICC Press
Abstract(s)Nowadays the efficiency of costs and resources planning in hospitals embody a critical role in the management of these units. Length Of Stay (LOS) is a good metric when the goal is to decrease costs and to optimize resources. In Intensive Care Units (ICU) optimization assumes even a greater importance derived from the high costs associated to inpatients. This study presents two data mining approaches to predict LOS in an ICU. The first approach considered the admission variables and some other physiologic variables collected during the first 24 hours of inpatient. The second approach considered admission data and supplementary clinical data of the patient (vital signs and laboratory results) collected in real-time. The results achieved in the first approach are very poor (accuracy of 73 %). However, when the prediction is made using the data collected in real-time, the results are very interesting (sensitivity of 96.104%). The models induced in second experiment are sensitive to the patient clinical situation and can predict LOS according to the monitored variables. Models for predicting LOS at admission are not suited to the ICU particularities. Alternatively, they should be induced in real-time, using online-learning and considering the most recent patient condition when the model is induced.
TypeConference paper
URIhttp://hdl.handle.net/1822/31357
ISBN9789897580505
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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