Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

Journal: The Journal of surgical research
PMID: 33784585

Abstract

BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who undergo coronary artery bypass grafting (CABG).

Authors

  • Austin J Triana
    Vanderbilt University School of Medicine, Nashville, Tennessee. Electronic address: austin.j.triana@vanderbilt.edu.
  • Rushikesh Vyas
    Vanderbilt University Medical Center, Department of Cardiac Surgery, Nashville, Tennessee; Vanderbilt University Medical Center, Department of Thoracic Surgery, Nashville, Tennessee.
  • Ashish S Shah
    Vanderbilt University Medical Center, Department of Cardiac Surgery, Nashville, Tennessee.
  • Vikram Tiwari
    Vanderbilt University Medical Center, Department of Anesthesiology, Nashville, Tennessee; Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, Tennessee; Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee; Vanderbilt University Medical Center Surgical Analytics, Nashville, Tennessee; Vanderbilt University Owen Graduate School of Management, Nashville, Tennessee.

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