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Updated: Jan 8, 2026

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

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

Published on: January 27, 2010

Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients.

Jason Guattery^1,2, Liane M Miller³, James J Irrgang^1,2

¹School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, United States.

BMC Musculoskeletal Disorders

|December 12, 2025

Summary

Machine learning models accurately predict prolonged post-operative opioid use in rotator cuff surgery patients. These transparent tools aid surgeons in preventing opioid prescriptions for at-risk individuals, addressing a significant healthcare challenge.

Keywords:

Machine learning in healthcare Post-operative opioid use Rotator cuff

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Last Updated: Jan 8, 2026

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

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Published on: October 10, 2018

Area of Science:

Orthopedic Surgery
Data Science
Machine Learning

Background:

Opioid overuse is a significant problem in the US, with surgery contributing to prescriptions.
Lack of clear prescribing guidelines exacerbates the issue.
Machine learning can help identify patients at risk for prolonged post-operative opioid use.

Purpose of the Study:

Develop accurate and transparent machine learning models.
Predict prolonged opioid use after rotator cuff surgery.

Main Methods:

Trained six machine learning models on 852 rotator cuff surgery patients.
Evaluated model predictive accuracy.
Used SHAP and LIME for model transparency and interpretation.

Main Results:

Four models exceeded 0.71 predictive accuracy.
Top models achieved 0.98 (XGBoost), 0.94 (Random Forest), and 0.74 (Decision Tree) accuracy.
SHAP and LIME provided insights into model predictions.

Conclusions:

Successfully developed predictive machine learning models for prolonged post-operative opioid use.
Achieved high predictive accuracy.
Enhanced model explainability and transparency.