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Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P.J., Ma, Q., et al. (2020) Improving Protein-Protein Interactions Prediction Accuracy Using Xgboost Feature Selection and Stacked Ensemble Classifier. Computers in Biology and Medicine, 123, Article 103899.
https://doi.org/10.1016/j.compbiomed.2020.103899

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• TITLE: Comparative Analysis of GABAergics vs. Opioids in Chronic Pain Management

  AUTHORS: Rocco de Filippis, Abdullah Al Foysal

  KEYWORDS: Chronic Pain Management, Opioid Treatments, GABAergic Medications, Machine Learning, SHAP Analysis, Quality of Life, Pain Reduction

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.11, November 18, 2024

  ABSTRACT: Background: Chronic pain management presents significant challenges in clinical practice, particularly in selecting pharmacological treatments that balance efficacy and safety. GABAergic and opioid medications are two commonly prescribed drug classes for pain relief, but their comparative effectiveness and safety profiles remain a topic of ongoing debate. Objectives: This study aims to compare the effectiveness and safety of GABAergic and opioid medications in managing chronic pain, using synthetic patient data and advanced machine learning models. Methods: Synthetic data simulating a diverse patient population were generated to reflect real-world variations in demographics, treatment regimens, and outcomes. Machine learning models, including Random Forest, Gradient Boosting, and Stacking, were applied to analyze the relationships between patient characteristics, treatment types, and outcomes such as pain reduction and quality of life. SHAP (SHapley Additive exPlanations) analysis was used to interpret the models and identify key predictors influencing treatment responses. Results: The Gradient Boosting model demonstrated strong predictive performance, with SHAP analysis highlighting features such as drug type, dosage, and patient age as significant factors influencing treatment outcomes. Opioid treatments were found to be more effective in pain reduction but associated with a higher risk of side effects, whereas GABAergics had a safer profile but were less potent in severe pain cases. Conclusions: This study underscores the value of machine learning in chronic pain management by providing insights into the trade-offs between the effectiveness and safety of GABAergic and opioid medications. These findings suggest that personalized treatment plans, informed by patient characteristics and model predictions, could optimize pain management while minimizing adverse effects.