Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.

Journal: JAMIA open
Published Date: May 26, 2025

Abstract

OBJECTIVE: Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for improving model reliability.

Authors

  • Lili M Schöler
    Department of Nursing, Medical Center-University of Freiburg, Freiburg 79106, Germany.
  • Lisa Graf
    Neurorobotics Lab, Department of Computer Science - University of Freiburg, Germany.
  • Antti Airola
    Department of Information Technology, University of Turku, Turku, Finland.
  • Alexander Ritzi
    Center of Implementing Nursing Care Innovations Freiburg, Medical Center - University of Freiburg, Germany.
  • Michael Simon
    Institute of Nursing Science, Department of Public Health, University of Basel, Basel 4056, Switzerland.
  • Laura-Maria Peltonen
    Nursing Science, University of Turku, and Turku University Hospital, Turku, Finland.

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