Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.

Journal: JMIR formative research
PMID: 39869898

Abstract

BACKGROUND: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.

Authors

  • Ajan Subramanian
    Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
  • Rui Cao
    Department of Cardiology of Lu'an People's Hospital, Lu'an Hospital of Anhui Medical University, Lu'an, China.
  • Emad Kasaeyan Naeini
    Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
  • Seyed Amir Hossein Aqajari
    Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.
  • Thomas D Hughes
    School of Nursing, University of California, Irvine, Irvine, CA, United States.
  • Michael-David Calderon
    Department of Anesthesiology & Perioperative Care, University of California Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
  • Kai Zheng
    University of California, Irvine, Irvine, CA, USA.
  • Nikil Dutt
    Department of Computer Science, University of California Irvine, Irvine, CA, USA; Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.
  • Pasi Liljeberg
    Department of Future Technologies, University of Turku, Turku, Finland.
  • Sanna Salanterä
    Nursing Science, University of Turku, and Turku University Hospital, Turku, Finland.
  • Ariana M Nelson
    Department of Anesthesiology and Perioperative Care, University of California, Irvine, Irvine, CA, United States.
  • Amir M Rahmani
    Department of Computer Science, University of California Irvine, Irvine, USA.

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