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Authors: Rui Veloso 1 ; Filipe Portela 1 ; Manuel Filipe Santos 1 ; Álvaro Silva 2 ; Fernando Rua 2 ; António Abelha 1 and José Machado 1

Affiliations: 1 University of Minho, Portugal ; 2 Centro Hospitalar do Porto and Hospital Santo António, Portugal

ISBN: 978-989-758-050-5

Keyword(s): Length of Stay, INTCare, Intensive Care Units, Data Mining, Real-Time.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Business Intelligence ; Intelligent Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Software Engineering ; Symbolic Systems ; Tools and Technology for Knowledge Management

Abstract: 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. (More)

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Paper citation in several formats:
Veloso, R.; Portela, F.; Filipe Santos, M.; Silva, Á.; Rua, F.; Abelha, A.; Machado, J. and Machado, J. (2014). Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units .In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014) ISBN 978-989-758-050-5, pages 245-254. DOI: 10.5220/0005083302450254

@conference{kmis14,
author={Rui Veloso. and Filipe Portela. and Manuel Filipe Santos. and Álvaro Silva. and Fernando Rua. and António Abelha. and José Machado. and José Machado.},
title={Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units },
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014)},
year={2014},
pages={245-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005083302450254},
isbn={978-989-758-050-5},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2014)
TI - Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units
SN - 978-989-758-050-5
AU - Veloso, R.
AU - Portela, F.
AU - Filipe Santos, M.
AU - Silva, Á.
AU - Rua, F.
AU - Abelha, A.
AU - Machado, J.
AU - Machado, J.
PY - 2014
SP - 245
EP - 254
DO - 10.5220/0005083302450254

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