Unraveling the physiological and psychosocial signatures of pain by machine learning.

Journal: Med (New York, N.Y.)
PMID: 39116869

Abstract

BACKGROUND: Pain is a complex subjective experience, strongly impacting health and quality of life. Despite many attempts to find effective solutions, present treatments are generic, often unsuccessful, and present significant side effects. Designing individualized therapies requires understanding of multidimensional pain experience, considering physical and emotional aspects. Current clinical pain assessments, relying on subjective one-dimensional numeric self-reports, fail to capture this complexity.

Authors

  • Noemi Gozzi
    Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
  • Greta Preatoni
    Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
  • Federico Ciotti
    Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
  • Michèle Hubli
    Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, 8008 Zürich, Switzerland.
  • Petra Schweinhardt
    Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, 8008 Zurich, Switzerland.
  • Armin Curt
    Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
  • Staniša Raspopović
    ETH Zurich, Department of Sciences and Techology, Switzerland.

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