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Related Experiment Video

Updated: Jul 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting Treatment Response to Pulsed Radiofrequency in Postherpetic Neuralgia Using an Explainable Machine

Yajun Li1, Baohong Shen1, Nannan Zhai1

  • 1Department of Pain, The First Affiliated Hospital of Henan Medical University, Xinxiang, 453100, People's Republic of China.

Journal of Pain Research
|July 15, 2026
PubMed
Summary

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An explainable machine learning model accurately predicts pulsed radiofrequency (PRF) treatment effectiveness for postherpetic neuralgia (PHN) patients. This tool aids in personalized risk assessment for better clinical decisions.

Area of Science:

  • Neurology
  • Pain Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Postherpetic neuralgia (PHN) presents heterogeneous responses to pulsed radiofrequency (PRF) treatment.
  • Predicting PRF efficacy is crucial for optimizing patient management.

Purpose of the Study:

  • To develop and validate an explainable machine learning (ML) model for predicting PRF efficacy in PHN patients.
  • To identify key predictors of PRF treatment response.

Main Methods:

  • A retrospective study of 404 PHN patients treated with PRF.
  • Feature selection using LASSO and Boruta algorithms.
  • Development and comparison of six ML algorithms, with performance evaluated by AUC and SHAP analysis.

Main Results:

Keywords:
SHapley Additive exPlanationsexplainable machine learningpostherpetic neuralgiapulsed radiofrequencytreatment response

Related Experiment Videos

Last Updated: Jul 16, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

  • The random forest (RF) model achieved an AUC of 0.878 in the validation set.
  • Key predictors identified include age, pain duration, diabetes mellitus, TNF-α, IL-6, and BDNF.
  • Older age, longer pain duration, DM, and higher TNF-α/IL-6 levels correlated with poor response; higher BDNF correlated with better response.

Conclusions:

  • An explainable ML model, specifically the RF model, demonstrates strong predictive performance for PRF efficacy in PHN.
  • The model can aid in individualized risk stratification and clinical decision-making.
  • Further prospective, multicenter validation is recommended.