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Authors: Filipe Portela ; Filipe Pinto and Manuel Filipe Santos

Affiliation: Universidade do Minho, Portugal

Keyword(s): Data Mining, KDD, Real-time, Pervasive, Intelligent Decision Support System, Intensive Care.

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

Abstract: The introduction of an Intelligent Decision Support System (IDSS) in a critical area like the Intensive Medicine is a complex and difficult process. In this area, their professionals don’t have much time to document the cases, because the patient direct care is always first. With the objective to reduce significantly the manual records and, enabling, at the same time, the possibility of developing an IDSS which can help in the decision making process, all data acquisition process and knowledge discovery in database phases were automated. From the data acquisition to the knowledge discovering, the entire process is autonomous and executed in real-time. On-line induced data mining models were used to predict organ failure and outcome. Preliminary results obtained with a limited population of patients showed that this approach can be applied successfully.

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Paper citation in several formats:
Portela, F.; Pinto, F. and Santos, M. (2012). Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine. In Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2012) - KMIS; ISBN 978-989-8565-31-0; ISSN 2184-3228, SciTePress, pages 81-88. DOI: 10.5220/0004141500810088

@conference{kmis12,
author={Filipe Portela. and Filipe Pinto. and Manuel Filipe Santos.},
title={Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2012) - KMIS},
year={2012},
pages={81-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004141500810088},
isbn={978-989-8565-31-0},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing (IC3K 2012) - KMIS
TI - Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine
SN - 978-989-8565-31-0
IS - 2184-3228
AU - Portela, F.
AU - Pinto, F.
AU - Santos, M.
PY - 2012
SP - 81
EP - 88
DO - 10.5220/0004141500810088
PB - SciTePress