Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care.

Journal: Computers in biology and medicine
Published Date: Apr 30, 2025

Abstract

BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including raw waveform signals, remains underexplored in clinical decision support.

Authors

  • Juan Miguel Lopez Alcaraz
    AI4Health Division, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, 26129, Lower Saxony, Germany. Electronic address: juan.lopez.alcaraz@uol.de.
  • Hjalmar Bouma
    Department of Internal Medicine, Department of Acute Care, and Department of Clinical Pharmacy & Pharmacology, University Medical Center Groningen, Hanzeplein 1, Groningen, 9713, Groningen, Netherlands. Electronic address: h.r.bouma@umcg.nl.
  • Nils Strodthoff
    Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany. Author to whom any correspondence should be addressed.

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