Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study.

Journal: Journal of medical Internet research
PMID: 40245392

Abstract

BACKGROUND: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology.

Authors

  • Fabrice Ferré
    Department of Anesthesia, Intensive Care and Perioperative Medicine, Purpan University Hospital, Toulouse, France.
  • Stéphanie Allassonnière
    Université Paris Cité, UFR Medecine, 75006 Paris, France.
  • Clément Chadebec
    Université Paris Cité, Unité Mixte de Recherche S1138, Institut national de recherche en sciences et technologies du numérique, Sorbonne University, Paris, France.
  • Vincent Minville
    Department of Anesthesia, Intensive Care and Perioperative Medicine, Purpan University Hospital, Toulouse, France.

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