HOURLY PREDICTION OF ORGAN FAILURE AND OUTCOME IN INTENSIVE CARE BASED ON DATA MINING TECHNIQUES

Marta Vilas-Boas, Manuel Filipe Santos, Filipe Portela, Álvaro Silva, Fernando Rua

Abstract

The use of Data Mining techniques makes possible to extract knowledge from high volumes of data. Currently, there is a trend to use Data Mining models in the perspective of intensive care to support physicians’ decision process. Previous results used offline data for the predicting organ failure and outcome for the next day. This paper presents the INTCare system and the recently generated Data Mining models. Advances in INTCare led to a new goal, prediction of organ failure and outcome for the next hour with data collected in real-time in the Intensive Care Unit of Hospital Geral de Santo António, Porto, Portugal. This experiment used Artificial Neural Networks, Decisions Trees, Logistic Regression and Ensemble Methods and we have achieved very interesting results, having proven that it is possible to use real-time data from the Intensive Care Unit to make highly accurate predictions for the next hour. This is a great advance in terms of intensive care, since predicting organ failure and outcome on an hourly basis will allow intensivists to have a faster and pro-active attitude in order to avoid or reverse organ failure.

References

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Paper Citation


in Harvard Style

Vilas-Boas M., Filipe Santos M., Portela F., Silva Á. and Rua F. (2010). HOURLY PREDICTION OF ORGAN FAILURE AND OUTCOME IN INTENSIVE CARE BASED ON DATA MINING TECHNIQUES . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 270-277. DOI: 10.5220/0002903802700277


in Bibtex Style

@conference{iceis10,
author={Marta Vilas-Boas and Manuel Filipe Santos and Filipe Portela and Álvaro Silva and Fernando Rua},
title={HOURLY PREDICTION OF ORGAN FAILURE AND OUTCOME IN INTENSIVE CARE BASED ON DATA MINING TECHNIQUES},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002903802700277},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - HOURLY PREDICTION OF ORGAN FAILURE AND OUTCOME IN INTENSIVE CARE BASED ON DATA MINING TECHNIQUES
SN - 978-989-8425-05-8
AU - Vilas-Boas M.
AU - Filipe Santos M.
AU - Portela F.
AU - Silva Á.
AU - Rua F.
PY - 2010
SP - 270
EP - 277
DO - 10.5220/0002903802700277