Journal of Neonatal Biology

Romesh Stanislaus

Publications

• Research Article
  Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?
  Author(s): Martina Mueller, Jonas S Almeida, Romesh Stanislaus and Carol L WagnerMartina Mueller, Jonas S Almeida, Romesh Stanislaus and Carol L Wagner
  
  Rationale: Though treatment of the prematurely born infant breathing with assistance of a mechanical ventilator has much advanced in the past decades, predicting extubation outcome at a given point in time remains challenging. Numerous studies have been conducted to identify predictors for extubation outcome; however, the rate of infants failing extubation attempts has not declined. Objective: To develop a decision-support tool for the prediction of extubation outcome in premature infants using a set of machine learning algorithms. Methods: A dataset assembled from 486 premature infants on mechanical ventilation was used to develop predictive models using machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Naïve Bayesian Classifier (NBC), Boosted Decision Trees (BDT), and Multivariable Logistic Regression (MLR). Performance of all m.. View More»
  DOI: 10.4172/2167-0897.1000118

  Abstract PDF