Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.

Journal: International journal of cardiology
Published Date: Aug 1, 2019

Abstract

OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients.

Authors

  • Tahani A Daghistani
    King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
  • Radwa Elshawi
    Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Sherif Sakr
    King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia.
  • Amjad M Ahmed
    King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia.
  • Abdullah Al-Thwayee
    King Abdulaziz Cardiac Center, King Abdulaziz Medical city for National Guard, Riyadh, Saudi Arabia.
  • Mouaz H Al-Mallah
    Division of Cardiovascular Medicine, Henry Ford Hospital, Detroit, Michigan; King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King AbdulAziz Cardiac Center, Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia. Electronic address: mouaz74@gmail.com.

Keywords

External Resources

Popular Topics
Recent Journals