Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BMC Medical Informatics and Decision Making
Martina MuellerJonas S Almeida

Abstract

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. 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 predicti...Continue Reading

References

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Citations

Jul 7, 2009·Archives de pédiatrie : organe officiel de la Sociéte française de pédiatrie·C DeguinesP Tourneux
Dec 11, 2007·Hypertension in Pregnancy : Official Journal of the International Society for the Study of Hypertension in Pregnancy·J MenziesP von Dadelszen
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Oct 29, 2014·The British Journal of General Practice : the Journal of the Royal College of General Practitioners·Neil PerkinsKath Checkland
Jan 10, 2012·Biological Reviews of the Cambridge Philosophical Society·Kai LorenzenMarc Mangel

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