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.
Cite this paper
Filippis, R. D. and Foysal, A. A. (2024). Comparative Analysis of GABAergics vs. Opioids in Chronic Pain Management. Open Access Library Journal, 11, e2388. doi: http://dx.doi.org/10.4236/oalib.1112388.
Clauw, D.J., Essex, M.N., Pitman, V. and Jones, K.D. (2019) Reframing Chronic Pain as a Disease, not a Symptom: Rationale and Implications for Pain Management. Postgraduate Medicine, 131, 185-198. https://doi.org/10.1080/00325481.2019.1574403
Jasmin, L., Wu, M. and Ohara, P. (2004) GABA Puts a Stop to Pain. Current Drug Target-CNS & Neurological Disorders, 3, 487-505. https://doi.org/10.2174/1568007043336716
Enna, S.J. and McCarson, K.E. (2006) The Role of GABA in the Media-tion and Perception of Pain. Advances in Pharmacology, 54, 1-27. https://doi.org/10.1016/s1054-3589(06)54001-3
Volkow, N.D. and Blanco, C. (2023) Substance Use Disorders: A Comprehensive Update of Classification, Epidemiology, Neurobiology, Clinical Aspects, Treatment and Prevention. World Psychiatry, 22, 203-229. https://doi.org/10.1002/wps.21073
Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., et al. (2024) Capabilities of Gemini Models in Medicine. arXiv: 2404.18416. https://doi.org/10.48550/arXiv.2404.18416
Wani, N.A., Kumar, R. and Bedi, J. (2024) Harnessing Fusion Modeling for Enhanced Breast Cancer Classification through Interpretable Artificial Intelligence and in-Depth Explanations. Engi-neering Applications of Artificial Intelligence, 136, Article 108939. https://doi.org/10.1016/j.engappai.2024.108939
Pezoulas, V.C., Zaridis, D.I., Mylona, E., Androutsos, C., Apostolidis, K., Tachos, N.S., et al. (2024) Synthetic Data Generation Methods in Healthcare: A Review on Open-Source Tools and Meth-ods. Computational and Structural Biotechnology Journal, 23, 2892-2910. https://doi.org/10.1016/j.csbj.2024.07.005
Abebe, M., Shin, Y., Noh, Y., Lee, S. and Lee, I. (2020) Machine Learning Approaches for Ship Speed Prediction Towards Energy Efficient Shipping. Applied Sciences, 10, Article 2325. https://doi.org/10.3390/app10072325
He, S., Leanse, L.G. and Feng, Y. (2021) Artificial Intelligence and Machine Learning Assisted Drug Delivery for Effective Treatment of Infectious Diseases. Advanced Drug Delivery Reviews, 178, Article 113922. https://doi.org/10.1016/j.addr.2021.113922
Weitzel, T., Beimborn, D. and König, W. (2006) A Unified Economic Model of Standard Diffusion: The Impact of Standardization Cost, Network Effects, and Network Topology. MIS Quarterly, 30, 489-514. https://doi.org/10.2307/25148770
Eichler, H., Abadie, E., Breckenridge, A., Flamion, B., Gustafsson, L.L., Leufkens, H., et al. (2011) Bridging the Efficacy-Effectiveness Gap: A Regulator’s Perspective on Addressing Variability of Drug Response. Nature Reviews Drug Discovery, 10, 495-506. https://doi.org/10.1038/nrd3501
Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., et al. (2012) Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance. Ecography, 36, 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Li, S., Harner, E.J. and Adjeroh, D.A. (2011) Random KNN Feature Selection—A Fast and Stable Alternative to Random Forests. BMC Bioinformatics, 12, Article No. 450. https://doi.org/10.1186/1471-2105-12-450
Strobl, C., Boulesteix, A., Kneib, T., Augustin, T. and Zeileis, A. (2008) Conditional Variable Importance for Ran-dom Forests. BMC Bioinformatics, 9, Article No. 307. https://doi.org/10.1186/1471-2105-9-307
Bentéjac, C., Csörgő, A. and Martínez-Muñoz, G. (2020) A Comparative Analysis of Gradient Boosting Algorithms. Artificial Intelligence Review, 54, 1937-1967. https://doi.org/10.1007/s10462-020-09896-5
Lopez-Martin, M., Carro, B. and Sanchez-Esguevillas, A. (2019) Neural Network Architecture Based on Gradient Boosting for IoT Traffic Prediction. Future Generation Computer Systems, 100, 656-673. https://doi.org/10.1016/j.future.2019.05.060
Hajihosseinlou, M., Maghsoudi, A. and Ghezelbash, R. (2024) Stacking: A Novel Data-Driven Ensemble Machine Learning Strategy for Prediction and Mapping of Pb-Zn Prospectivity in Varcheh District, West Iran. Expert Systems with Applications, 237, Article 121668. https://doi.org/10.1016/j.eswa.2023.121668
Cui, C., Hu, M., Weir, J.D. and Wu, T. (2016) A Recommendation Sys-tem for Meta-Modeling: A Meta-Learning Based Approach. Expert Systems with Applications, 46, 33-44. https://doi.org/10.1016/j.eswa.2015.10.021
Xu, Y. and Goodacre, R. (2018) On Splitting Training and Val-idation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing, 2, 249-262. https://doi.org/10.1007/s41664-018-0068-2
Cichosz, P. (2011) Assessing the Quality of Classification Models: Per-formance Measures and Evaluation Procedures. Open Engineering, 1, 132-158. https://doi.org/10.2478/s13531-011-0022-9
Hsu, M., Lessmann, S., Sung, M., Ma, T. and Johnson, J.E.V. (2016) Bridging the Divide in Financial Market Forecasting: Machine Learners vs. Financial Economists. Expert Systems with Appli-cations, 61, 215-234. https://doi.org/10.1016/j.eswa.2016.05.033
Pearce, J. and Ferrier, S. (2000) Evaluating the Predictive Performance of Habitat Models Developed Using Logistic Regression. Ecological Modelling, 133, 225-245. https://doi.org/10.1016/s0304-3800(00)00322-7
Powers, D.M.W. (2020) Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. arXiv: 2010.16061. https://doi.org/10.48550/arXiv.2010.16061
Jeni, L.A., Cohn, J.F. and De La Torre, F. (2013) Facing Imbalanced Data—Recommendations for the Use of Performance Metrics. 2013 Humaine Association Conference on Affective Compu-ting and Intelligent Interaction, Geneva, 2-5 September 2013, 245-251. https://doi.org/10.1109/acii.2013.47
Lobo, J.M., Jiménez-Valverde, A. and Real, R. (2007) AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Global Ecology and Biogeography, 17, 145-151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Stenwig, E., Salvi, G., Rossi, P.S. and Skjærvold, N.K. (2022) Comparative Analysis of Explainable Machine Learning Prediction Models for Hospital Mortality. BMC Medical Research Methodology, 22, Article No. 53. https://doi.org/10.1186/s12874-022-01540-w
Feng, D., Wang, W., Mangalathu, S. and Taciroglu, E. (2021) Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls. Journal of Structural Engineering, 147. https://doi.org/10.1061/(asce)st.1943-541x.0003115
Antoniadi, A.M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B.A., et al. (2021) Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences, 11, Article 5088. https://doi.org/10.3390/app11115088
Tonekaboni, S., Joshi, S., McCradden, M.D. and Goldenberg, A. (2019) What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. arXiv: 1905.05134. https://doi.org/10.48550/arXiv.1905.05134
Kuo, N.I., Garcia, F., Sönnerborg, A., Böhm, M., Kaiser, R., Zazzi, M., et al. (2023) Generating Synthetic Clinical Data That Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example Using Antiretroviral Therapy for HIV. Journal of Biomedical Informatics, 144, Article 104436. https://doi.org/10.1016/j.jbi.2023.104436
Ziegler, G., Ridgway, G.R., Dahnke, R. and Gaser, C. (2014) Individualized Gaussian Process-Based Prediction and Detection of Local and Global Gray Matter Abnormalities in Elderly Subjects. Neu-roImage, 97, 333-348. https://doi.org/10.1016/j.neuroimage.2014.04.018
Sheiner, L.B., Rosenberg, B. and Melmon, K.L. (1972) Modelling of Individual Pharmacokinetics for Computer-Aided Drug Dosage. Computers and Biomedi-cal Research, 5, 441-459. https://doi.org/10.1016/0010-4809(72)90051-1
Maharana, K., Mondal, S. and Nemade, B. (2022) A Review: Data Pre-Processing and Data Augmentation Techniques. Global Transi-tions Proceedings, 3, 91-99. https://doi.org/10.1016/j.gltp.2022.04.020
Ahmad, G.N., Shafiullah, Fatima, H., Abbas, M., Rahman, O., Imdadullah,, et al. (2022) Mixed Machine Learning Approach for Efficient Prediction of Human Heart Dis-ease by Identifying the Numerical and Categorical Features. Applied Sciences, 12, Article 7449. https://doi.org/10.3390/app12157449
Thévenot, E.A., Roux, A., Xu, Y., Ezan, E. and Junot, C. (2015) Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. Journal of Proteome Research, 14, 3322-3335. https://doi.org/10.1021/acs.jproteome.5b00354
Hussain, M., Sahudin, S. and Yussof, I. (2020) Exploring the Use of Computer-Aided Learning Modules (CAL) to Enhance the Teaching and Learning of Pharmacokinetics to Pharmacy Stu-dents. Journal of Young Pharmacists, 12, 354-359. https://doi.org/10.5530/jyp.2020.12.91
Smith, L.E., Webster, R.K. and Rubin, G.J. (2020) A Systematic Review of Factors Associated with Side-Effect Expectations from Medical Interven-tions. Health Expectations, 23, 731-758. https://doi.org/10.1111/hex.13059
Curtis, A., Smith, T., Ziganshin, B. and Elefteriades, J. (2016) The Mystery of the Z-Score. AORTA, 4, 124-130. https://doi.org/10.12945/j.aorta.2016.16.014
Schielzeth, H. (2010) Simple Means to Improve the Interpretability of Regression Coefficients. Methods in Ecology and Evolution, 1, 103-113. https://doi.org/10.1111/j.2041-210x.2010.00012.x
Kubota, K.J., Chen, J.A. and Little, M.A. (2016) Machine Learn-ing for Large-Scale Wearable Sensor Data in Parkinson’s Disease: Concepts, Promises, Pitfalls, and Futures. Movement Dis-orders, 31, 1314-1326. https://doi.org/10.1002/mds.26693
Hukkelhoven, C.W.P.M., Steyerberg, E.W., Rampen, A.J.J., Farace, E., Habbema, J.D.F., Marshall, L.F., et al. (2003) Patient Age and Outcome Follow-ing Severe Traumatic Brain Injury: An Analysis of 5600 Patients. Journal of Neurosurgery, 99, 666-673. https://doi.org/10.3171/jns.2003.99.4.0666
Smith, E.M.L., Pang, H., Cirrincione, C., Fleishman, S., Paskett, E.D., Ahles, T., et al. (2013) Effect of Duloxetine on Pain, Function, and Quality of Life among Patients with Chemothera-py-Induced Painful Peripheral Neuropathy. JAMA, 309, 1359-1367. https://doi.org/10.1001/jama.2013.2813
White, D.B., Walawander, C.A., Tung, Y. and Grasela, T.H. (1991) An Evaluation of Point and Interval Estimates in Population Pharmacokinetics Using Nonmem Analysis. Journal of Pharmacokinetics and Biopharmaceutics, 19, 87-112. https://doi.org/10.1007/bf01062194
Zhang, C., Manheim, F.T., Hinde, J. and Grossman, J.N. (2005) Statistical Characterization of a Large Geochemical Database and Effect of Sample Size. Applied Geochemistry, 20, 1857-1874. https://doi.org/10.1016/j.apgeochem.2005.06.006
Clemen, R.T. (1989) Combining Forecasts: A Review and Annotated Bibliography. International Journal of Forecasting, 5, 559-583. https://doi.org/10.1016/0169-2070(89)90012-5
Prasad, A.M., Iverson, L.R. and Liaw, A. (2006) Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Predic-tion. Ecosystems, 9, 181-199. https://doi.org/10.1007/s10021-005-0054-1
Callens, A., Morichon, D., Abadie, S., Delpey, M. and Liquet, B. (2020) Using Random Forest and Gradient Boosting Trees to Improve Wave Forecast at a Specific Location. Applied Ocean Research, 104, Article 102339. https://doi.org/10.1016/j.apor.2020.102339
Svetnik, V., Wang, T., Tong, C., Liaw, A., Sheridan, R.P. and Song, Q. (2005) Boosting: An Ensemble Learning Tool for Compound Classi-fication and QSAR Modeling. Journal of Chemical Information and Modeling, 45, 786-799. https://doi.org/10.1021/ci0500379
Stefana, E. and Paltrinieri, N. (2021) ProMetaUS: A Proactive Meta-Learning Uncertainty-Based Framework to Select Models for Dynamic Risk Man-agement. Safety Science, 138, Article 105238. https://doi.org/10.1016/j.ssci.2021.105238
Munoz, D., Bagnell, J.A. and Hebert, M. (2010) Stacked Hierarchical Labeling. Computer Vision—ECCV 2010, Crete, 5-11 September 2010 57-70. https://doi.org/10.1007/978-3-642-15567-3_5
Krstinić, D., Braović, M., Šerić, L. and Božić-Štulić, D. (2020) Mul-ti-Label Classifier Performance Evaluation with Confusion Matrix. Computer Science & Information Technology, 1, 1-14. https://doi.org/10.5121/csit.2020.100801
Wynants, L., van Smeden, M., McLernon, D.J., Timmerman, D., Steyer-berg, E.W. and Van Calster, B. (2019) Three Myths about Risk Thresholds for Prediction Models. BMC Medicine, 17, Article No. 192. https://doi.org/10.1186/s12916-019-1425-3
Santika, T. (2010) Assessing the Effect of Prevalence on the Predictive Performance of Species Distribution Models Using Simulated Data. Global Ecology and Biogeography, 20, 181-192. https://doi.org/10.1111/j.1466-8238.2010.00581.x
Agrawal, A., Viktor, H.L. and Paquet, E. (2015) SCUT: Multi-Class Imbalanced Data Classification Using SMOTE and Cluster-Based Undersampling. 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), Lisbon, 12-14 November 2015, 226-234. https://doi.org/10.5220/0005595502260234
Bekkar, M. and Alitouche, T.A. (2013) Imbalanced Data Learning Approaches Review. International Journal of Data Mining & Knowledge Management Process, 3, 15-33. https://doi.org/10.5121/ijdkp.2013.3402
Lundberg, S.M. and Lee, S.-I. (2017) A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 4768-4777.
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H. and Wortman Vaughan, J. (2020) Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, 25-30 April 2020, 1-14. https://doi.org/10.1145/3313831.3376219
Tougui, I., Jilbab, A. and Mhamdi, J.E. (2021) Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications. Healthcare Infor-matics Research, 27, 189-199. https://doi.org/10.4258/hir.2021.27.3.189
Raschka, S. (2018) Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv: 1811.12808 https://doi.org/10.48550/arXiv.1811.12808
Delgadillo, J., Moreea, O. and Lutz, W. (2016) Different People Re-spond Differently to Therapy: A Demonstration Using Patient Profiling and Risk Stratification. Behaviour Research and Therapy, 79, 15-22. https://doi.org/10.1016/j.brat.2016.02.003
Rea, K., Roche, M. and Finn, D.P. (2007) Su-praspinal Modulation of Pain by Cannabinoids: The Role of GABA and Glutamate. British Journal of Pharmacology, 152, 633-648. https://doi.org/10.1038/sj.bjp.0707440
Hofhuis, J.G., Spronk, P.E., van Stel, H.F., Schrijvers, A.J. and Bak-ker, J. (2007) Quality of Life before Intensive Care Unit Admission Is a Predictor of Survival. Critical Care, 11, Article No. R78. https://doi.org/10.1186/cc5970
Khan, O., Badhiwala, J.H., Witiw, C.D., Wilson, J.R. and Fehlings, M.G. (2021) Machine Learning Algorithms for Prediction of Health-Related Quality-of-Life after Surgery for Mild Degenerative Cervical Myelopathy. The Spine Journal, 21, 1659-1669. https://doi.org/10.1016/j.spinee.2020.02.003
Labianca, R., Sar-zi-Puttini, P., Zuccaro, S.M., Cherubino, P., Vellucci, R. and Fornasari, D. (2012) Adverse Effects Associated with Non-Opioid and Opioid Treatment in Patients with Chronic Pain. Clinical Drug Investigation, 32, 53-63. https://doi.org/10.2165/11630080-000000000-00000
Tucker, A., Wang, Z., Rotalinti, Y. and Myles, P. (2020) Gen-erating High-Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software. npj Digital Medicine, 3, Article No. 147. https://doi.org/10.1038/s41746-020-00353-9
El Emam, K., Mosquera, L. and Hoptroff, R. (2020) Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data. O’Reilly Media.
Shi, Y. and Wu, W. (2023) Multimodal Non-Invasive Non-Pharmacological Therapies for Chronic Pain: Mechanisms and Progress. BMC Medicine, 21, Article No. 372. https://doi.org/10.1186/s12916-023-03076-2
Sud, A., Buchman, D.Z., Furlan, A.D., Selby, P., Spithoff, S.M. and Upshur, R.E.G. (2022) Chronic Pain and Opioid Prescribing: Three Ways for Navigating Complexity at the Clini-cal-Population Health Interface. American Journal of Public Health, 112, S56-S65. https://doi.org/10.2105/ajph.2021.306500
