Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables.

Journal: PloS one
Published Date: Dec 28, 2015

Abstract

BACKGROUND: Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.

Authors

  • Rocco J LaFaro
    Department of Surgery, New York Medical College, Valhalla, New York, United States of America.
  • Suryanarayana Pothula
    Department of Anesthesiology, New York Medical College, Valhalla, New York, United States of America.
  • Keshar Paul Kubal
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.
  • Mario Emil Inchiosa
    Revolution Analytics, Inc., Mountain View, California, United States of America.
  • Venu M Pothula
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.
  • Stanley C Yuan
    Department of Anesthesiology, New York Medical College, Valhalla, New York, United States of America.
  • David A Maerz
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.
  • Lucresia Montes
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.
  • Stephen M Oleszkiewicz
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.
  • Albert Yusupov
    Department of Anesthesiology, New York Medical College, Valhalla, New York, United States of America.
  • Richard Perline
    The SAS Institute, Cary, North Carolina, United States of America.
  • Mario Anthony Inchiosa
    Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.

Keywords

External Resources

Popular Topics
Recent Journals