Artificial neural networks as prediction tools in the critically ill

doi:10.1186/cc3507

Critical Care

Volume 9
Issue 2

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Commentary

Artificial neural networks as prediction tools in the critically ill

Gilles Clermont

Co-Director, The CRISMA (Clinical Research, Investigation, and Systems Modeling of Acute Illness) Laboratory, Department of Critical Care Medicine, and Medical Director of The Center for Inflammatory and Regenerative Modeling, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

author email corresponding author email

Critical Care 2005, 9:153-154doi:10.1186/cc3507


Published:	3 March 2005

See related research article http://ccforum.com/content/9/2/R150

Abstract

The past 25 years have witnessed the development of improved tools with which to predict short-term and long-term outcomes after critical illness. The general paradigm for constructing the best known tools has been the logistic regression model. Recently, a variety of alternative tools, such as artificial neural networks, have been proposed, with claims of improved performance over more traditional models in particular settings. However, these newer methods have yet to demonstrate their practicality and usefulness within the context of predicting outcomes in the critically ill.