Improving ED admissions forecasting by using generative AI: An approach based on DGAN.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 8, 2024

Abstract

BACKGROUND AND OBJECTIVE: Generative Deep Learning has emerged in recent years as a significant player in the Artificial Intelligence field. Synthesizing new data while maintaining the features of reality has revolutionized the field of Deep Learning, proving to be particularly useful in contexts where obtaining data is challenging. The objective of this study is to employ the DoppelGANger algorithm, a cutting-edge approach based on Generative Adversarial Networks for time series, to enhance patient admissions forecasting in a hospital Emergency Department.

Authors

  • Hugo Álvarez-Chaves
    Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain. Electronic address: hugo.alvarezc@uah.es.
  • Marco Spruit
    Department of Computing and Information Sciences, Utrecht University, Utrecht, Netherlands.
  • María D R-Moreno
    Universidad de Alcalá, Escuela Politécnica Superior, 28805, Madrid, Spain. Electronic address: malola.rmoreno@uah.es.

Keywords

External Resources

Popular Topics
Recent Journals