Development of Machine Learning–Based Models to Predict Treatment Response to Spinal Cord Stimulation-Reference-Cited by-同舟云学术

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Development of Machine Learning–Based Models to Predict Treatment Response to Spinal Cord Stimulation

Published:2022-02-22 Issue:5 Volume:90 Page:523-532
ISSN:0148-396X
Container-title:Neurosurgery
language:en
Short-container-title:

Author:

Hadanny Amir,Harland Tessa,Khazen Olga,DiMarzio Marisa,Marchese Anthony,Telkes Ilknur,Sukul Vishad,Pilitsis Julie G.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Neurology (clinical),Surgery

Reference30 articles.

1. Increased spinal cord stimulator use and continued opioid treatment among injured workers: a regional pilot study;Leung;J Occup Environ Med.,2020

2. Spinal cord stimulation for chronic refractory pain: long-term effectiveness and safety data from a multicentre registry;Brinzeu;Eur J Pain.,2019

3. Long-term outcome of spinal cord stimulation in failed back surgery syndrome: 20 years of experience with 224 consecutive patients;Nissen;Neurosurgery.,2019

4. A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results;Bagherzadeh-Khiabani;J Clin Epidemiol.,2016

5. Duloxetine improves spinal cord stimulation outcomes for chronic pain;Prabhala;Neuromodulation.,2019

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1. Socioeconomic Determinants of Initiating Neuromodulation for Chronic Pain: A Systematic Review;Neuromodulation: Technology at the Neural Interface;2024-09

2. The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain;Current Pain and Headache Reports;2024-06-01

3. Variables associated with nonresponders to high‐frequency (10 kHz) spinal cord stimulation;Pain Practice;2023-12-11

4. Dorsal Root Entry Zone Lesioning Following Unresponsive Spinal Cord Stimulation for Post-Traumatic Neuropathic Pain;World Neurosurgery;2023-07

5. Machine learning predictive models in neurosurgery: an appraisal based on the TRIPOD guidelines. Systematic review;Neurosurgical Focus;2023-06

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