Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.

Journal: A&A practice
Published Date:

Abstract

BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predicting postinduction hypotension, with neural networks providing a robust and accurate approach to model complex relationships. This study aims to use machine learning to suggest anesthetic doses, both generalized to an average patient population and personalized for specific patients, incorporating domain knowledge into the modeling process.

Authors

  • Kathryn Sarullo
    Department of Computer Science and Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Muntaha Samad
    Department of Computer Science, University of California, Irvine, California 92697, United States.
  • Samir Kendale
    Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.
  • S Joshua Swamidass
    Department of Computer Science and Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri.