New advances and validation of knowledge management tools for critical care using classifier techniques.

Abstract

An earlier version (2.0) of the case-based reasoning (CBR) tool, called IDEAS for ICU's, allowed users to compare the ten closest matching cases to the newest patient admission, using a large database of intensive care patient records, and physician-selected matching-weights [1,2]. The new version incorporates matching-weights, which have been determined quantitatively. A faster CBR matching engine has also been incorporated into the new CBR. In a second approach, a back-propagation, feed-forward artificial neural network estimated two classes of the outcome "duration of artificial ventilation" for a subset of the database used for the CBR work. Weight-elimination was successfully applied to reduce the number of input variables and speed-up the estimation of outcomes. New experiments examined the impact of using a different number of input variables on the performance of the ANN, measured by correct classification rates (CCR) and the Average Squared Error (ASE).