Organ Failure Diagnosis by Artificial Neural Networks

Á. Silva, P. Cortez, M. Santos, L. Gomes, and J. Neves (Portugal)


Intensive Care Medicine, Classification, Multilayer Perceptrons, Out of Range Measurements, Sequential Organ Failure Assessment


In recent years, Clinical Data Mining has gained an in creasing acceptance by the research community, due to its potential to find answers that could extend life or give com fort to ill persons. In particular, the use of tools such as Ar tificial Neural Networks, which have been mostly used in classification tasks. The present work reports the adoption of these techniques for the prediction of organ dysfunction of Intensive Care Unit patients. The novelty of this ap proach is due to the use intermediate outcomes, defined by the Out of Range Measurements of four bedside monitored variables, which obtained an overall accuracy of 70%.

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