%0 Journal Article
%T Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants
%A Martina Mueller
%A Carol L Wagner
%A David J Annibale
%A Rebecca G Knapp
%A Thomas C Hulsey
%A Jonas S Almeida
%J BMC Medical Informatics and Decision Making
%D 2006
%I BioMed Central
%R 10.1186/1472-6947-6-11
%X A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN.CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0¨C1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool.State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.Approximately 470,000 infants (~12%) are born prematurely in the US each year. Virtually all infants born at ¡Ü27 weeks gestation,
%U http://www.biomedcentral.com/1472-6947/6/11