Artificial neural networks as prediction tools in the critically ill
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Correspondence: Gilles Clermont clermontg@upmc.edu
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
Critical Care 2005, 9:153-154 doi:10.1186/cc3507
See related research article http://ccforum.com/content/9/2/R150
Published: 3 March 2005Abstract
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.