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Authors: Manuel Santos and Filipe Portela

Affiliation: Universidade do Minho, Portugal

Keyword(s): Ubiquitous Data Mining, Real-time Intelligent Decision Support Systems, Organ failure prediction, Clinical Data Mining, Intensive Care Environment.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Agents and Internet Computing ; Ubiquitous Computing

Abstract: Ubiquitous Data Mining and Intelligent Decision Support Systems are gaining interest by both computer science researchers and intensive care doctors. Previous work contributed with Data Mining models to predict organ failure and outcome of patients in order to support and guide the clinical decision based on the notion of critical events and the data collected from monitors in real-time. This paper addresses the study of the impact of the Modified Early Warning Score, a simple physiological score that may allow improvements in the quality and safety of management provided to surgical ward patients, in the prediction sensibility. The feature selection and data pre-processing are also detailed. Results show that for some variables associated to this score the impact is minimal.

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Paper citation in several formats:
Santos, M. and Portela, F. (2011). ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing. In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-8425-53-9; ISSN 2184-4992, SciTePress, pages 261-266. DOI: 10.5220/0003507302610266

@conference{iceis11,
author={Manuel Santos. and Filipe Portela.},
title={ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2011},
pages={261-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003507302610266},
isbn={978-989-8425-53-9},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - ENABLING UBIQUITOUS DATA MINING IN INTENSIVE CARE - Features Selection and Data Pre-processing
SN - 978-989-8425-53-9
IS - 2184-4992
AU - Santos, M.
AU - Portela, F.
PY - 2011
SP - 261
EP - 266
DO - 10.5220/0003507302610266
PB - SciTePress