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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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Related Experiment Video

Updated: Sep 7, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

Machine Learning and Pain Outcomes.

Tessa Harland¹, Amir Hadanny¹, Julie G Pilitsis²

¹Department of Neurosurgery, Albany Medical College, 47 New Scotland Ave, Physicians Pavilion, 1st Floor, Albany, NY, 12208, USA.

Neurosurgery Clinics of North America

|June 19, 2022

Summary

This summary is machine-generated.

Machine learning (ML) offers advanced data analysis for pain management, improving patient selection for treatments and identifying biomarkers to objectively measure pain. This technology enhances pain research and clinical applications.

Keywords:

Biomarker Machine learning Pain management Pain outcomes Patient selection Prediction

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Last Updated: Sep 7, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Published on: January 27, 2010

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

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

Published on: April 14, 2016

Area of Science:

Pain Management
Data Science
Biomarker Discovery

Background:

Machine learning (ML) is a rapidly advancing field with significant potential in healthcare.
Pain management research currently faces challenges in objective patient assessment and treatment selection.

Purpose of the Study:

To provide a comprehensive overview of machine learning (ML) applications in pain management.
To highlight ML's role in refining patient selection for pain treatments.
To discuss ML's utility in identifying objective pain biomarkers.

Main Methods:

Review of current literature on machine learning applications in pain management.
Analysis of ML techniques used for patient stratification.
Exploration of ML algorithms for biomarker discovery in pain.

Main Results:

ML aids in identifying suitable candidates for invasive pain management procedures.
ML contributes to the discovery of objective biomarkers for pain quantification.
These applications aim to improve treatment efficacy and patient outcomes.

Conclusions:

Machine learning presents a powerful tool for advancing pain management strategies.
Further integration of ML in pain research promises more personalized and effective treatments.
Objective pain assessment through ML-driven biomarkers is a key area for future development.