Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach
This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings.
Authors
- Nikil Dutt (ORCID: https://orcid.org/0000-0002-3060-8119)
- Pasi Liljeberg (ORCID: https://orcid.org/0000-0002-9392-3589)
- Emad Kasaeyan Naeini (ORCID: https://orcid.org/0000-0002-7438-2641)
- Seyed Amir Hossein Aqajari (ORCID: https://orcid.org/0000-0003-1747-6980)
- Ajan Subramanian (ORCID: https://orcid.org/0000-0003-3253-1300)
- Amir M. Rahmani (ORCID: https://orcid.org/0000-0003-0725-1155)
- Kai Zheng (ORCID: https://orcid.org/0000-0003-4121-4948)
- Ariana M. Nelson (ORCID: https://orcid.org/0000-0003-1575-1635)
- Sanna Salanterä (ORCID: https://orcid.org/0000-0003-2529-6699)
- Rui Cao (ORCID: https://orcid.org/0000-0001-9295-8299)
- Michael-David Calderon (ORCID: https://orcid.org/0000-0001-6824-6945)
- Thomas Hughes (ORCID: https://orcid.org/0000-0002-0651-9394)
Institutions
- University of Turku (FI)
- Kansas City University (US)
- University of California, Irvine (US)
- Turku University Hospital (FI)
- Institute for the Future (US)
- University of Missouri–Kansas City (US)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2024-11-29
- DOI
- https://doi.org/10.2196/67969
- Citations
- 6
- Primary Topic
- Anesthesia and Pain Management
- Type
- article
- Field-Weighted Citation Impact
- 3.44