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Analgesia and Pain Management

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.

Ajan Subramanian¹, Rui Cao², Emad Kasaeyan Naeini¹

¹Department of Computer Science, University of California, Irvine, Irvine, CA, United States.

JMIR Formative Research

|January 27, 2025

Summary

This summary is machine-generated.

This study developed a machine learning framework using biosignals for objective postoperative pain assessment. The multimodal approach achieved over 80% accuracy, improving pain monitoring in clinical settings.

Keywords:

acute pain behavioral pain clinical pain management electrocardiogram electrodermal activity electromyogram health care machine learning approach machine learning–based framework multimodal information fusion multimodal machine learning–based framework pain assessment pain intensity pain intensity recognition pain measurement pain monitoring pain recognition self-reported pain level signal processing weak supervision

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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

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Published on: April 14, 2016

Area of Science:

Biomedical Engineering
Machine Learning
Pain Management

Background:

Effective acute pain management is crucial for postoperative patients, especially those unable to self-report pain.
Current pain assessment methods (subjective reports, behavioral tools) lack objectivity and consistency.
Multimodal pain assessment using physiological and behavioral data offers a path to more accurate pain measurement, but real-world clinical data is limited.

Purpose of the Study:

To develop and evaluate a multimodal machine learning framework for objective pain assessment in postoperative patients.
To utilize biosignals like electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) in real clinical settings.
To address challenges in clinical pain monitoring, including motion artifacts and imbalanced data.

Main Methods:

The iHurt study involved 25 postoperative patients, collecting multimodal biosignals and Numerical Rating Scale pain scores.
Data preprocessing included noise filtering and feature extraction, combining handcrafted and autoencoder-derived features.
Machine learning classifiers were trained using weak supervision and oversampling to manage sparse and imbalanced pain data.

Main Results:

Multimodal pain recognition models achieved an average balanced accuracy exceeding 80% across pain levels.
Respiration rate (RR) models showed strong performance, especially for lower pain intensities; electromyogram was effective for higher intensities.
While single modalities like RR performed well, the multimodal framework demonstrated improved overall accuracy compared to previous studies.

Conclusions:

A novel multimodal machine learning framework for objective postoperative pain recognition was developed.
Integrating multiple biosignal modalities shows significant potential for enhancing pain assessment accuracy.
The framework offers valuable applications for real-world clinical pain monitoring.