Authors:
Álvaro Silva
1
;
Paulo Cortez
2
;
Manuel Santos
2
;
Lopes Gomes
3
and
José Neves
4
Affiliations:
1
Hospital Geral de Santo António, Portugal
;
2
DSI, Universidade do Minho, Portugal
;
3
Inst. de Ciências Biomédicas Abel Salazar, Portugal
;
4
DI, Universidade do Minho, Portugal
Keyword(s):
Intensive Care Medicine, Classification, Clinical Data Mining, Multilayer Perceptrons.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In the past years, the Clinical Data Mining arena has suffered a remarkable development, where intelligent data analysis tools, such as Neural Networks, have been successfully applied in the design of medical systems. In this work, Neural Networks are applied to the prediction of organ dysfunction in Intensive Care Units. The novelty of this approach comes from the use of adverse events, which are triggered from four bedside alarms, being achieved an overall predictive accuracy of 70%.