Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach

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 inconsi...

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Bibliographic Details
Published in:	JMIR formative research Vol. 9; p. e67969
Main Authors:	Subramanian, Ajan, Cao, Rui, Naeini, Emad Kasaeyan, Aqajari, Seyed Amir Hossein, Hughes, Thomas D, Calderon, Michael-David, Zheng, Kai, Dutt, Nikil, Liljeberg, Pasi, Salanterä, Sanna, Nelson, Ariana M, Rahmani, Amir M
Format:	Journal Article
Language:	English
Published:	Canada JMIR Publications 27.01.2025
Subjects:	Adult Aged Behavior Chronic pain Data collection Datasets Dementia Electrocardiography Electroencephalography Female Heart rate Humans Machine Learning Male Medical research Middle Aged Minority & ethnic groups Original Paper Pain Measurement - methods Pain, Postoperative - diagnosis Patients Physiology Software United States > US California machine learning approach pain recognition acute pain signal processing pain monitoring pain assessment electrodermal activity pain intensity multimodal machine learning–based framework electromyogram self-reported pain level weak supervision multimodal information fusion machine learning–based framework clinical pain management electrocardiogram pain measurement pain intensity recognition behavioral pain health care
ISSN:	2561-326X, 2561-326X
Online Access:	Get full text
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