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Therapeutic

🧠 Machine-Learning Model Predicts Pulsed Radiofrequency Response in Zoster Pain

June 18, 2026 (EMFS)
Jun 16, 2026 (original)
Source: Pain Ther
Source category: Therapeutic
AI use
A Pain and Therapy study of 1,773 patients with zoster-associated pain reports a CatBoost model for predicting response to pulsed radiofrequency treatment. The authors say the model combines clinical measures with lipid and inflammatory biomarkers to support more individualized pain management. All five predictors — age, baseline pain score, preoperative opioid use, apolipoprotein B, and neutrophil-to-monocyte ratio — are patient-level characteristics; the model does not include treatment protocol variables. The five were selected from an initial pool of 47 clinical and laboratory variables through a three-stage feature selection pipeline.
🔬 This research is covered in EMF Signal's section on therapeutic EMF applications. It investigates controlled use of electromagnetic fields as potential treatment, not environmental exposure.
Note: The article does not describe the PRF treatment characteristics in terms of frequency, duration or other characteristics.

"Pulsed radiofrequency (PRF) is a pivotal neuromodulation strategy for zoster-associated pain (ZAP); however, clinical outcomes exhibit significant interindividual heterogeneity."

— Lei Zhang et al.

"This study aimed to identify robust predictors and develop a transparent machine learning (ML) framework to forecast treatment response, thereby facilitating individualized pain management."

— Lei Zhang et al.

Source

A Novel Machine Learning Signature Incorporating Lipid and Inflammatory Biomarkers to Predict Pulsed Radiofrequency Efficacy in Zoster-Associated Pain. pubmed.ncbi.nlm.nih.gov

📄 Underlying Research

A Novel Machine Learning Signature Incorporating Lipid and Inflammatory Biomarkers to Predict Pulsed Radiofrequency Efficacy in Zoster-Associated Pain.

Lei Zhang et al. (2026) Pain and therapy Journal Level 1ⓘ

🔗 DOI 📚 PubMed

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