Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU.

Journal: Respiratory care
Published Date: Nov 1, 2015

Abstract

BACKGROUND: Twenty-five to 40% of patients pass a spontaneous breathing trial (SBT) but fail to wean from mechanical ventilation. There is no single appropriate and convenient predictor or method that can help clinicians to accurately predict weaning outcomes. This study designed an artificial neural network (ANN) model for predicting successful extubation in mechanically ventilated patients.

Authors

  • Hung-Ju Kuo
    Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. Graduate Institute of Biomedical Informatics.
  • Hung-Wen Chiu
    Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan. Electronic address: hwchiu@tmu.edu.tw.
  • Chun-Nin Lee
    Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Tzu-Tao Chen
    Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan.
  • Chih-Cheng Chang
    Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan.
  • Mauo-Ying Bien
    School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan. Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan. mybien@tmu.edu.tw.

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