A precision medicine approach for non-opioid pain therapy using a combination of multi-objective optimization and support vector regression

Chronic pain has been linked with negative impacts on psychological and social factors, with mortality and several diseases. Lately, emphasis has been focused on non-opioid treatments to overcome its addictive nature and other side effects. To address this, the OPERA study evaluated the effectiveness of topical analgesics as an alternative method to opioids for pain therapy. Initial results showed that topical analgesics have significant benefits for the majority of chronic pain patients. However, there were segments of patients who did not seem to benefit from prescribed therapy and some participants whose situation deteriorated after the intervention. In the present study, we are exploring the potential of machine learning methods to classify chronic pain patients into those who will benefit from topical analgesics treatment and those who will not, in order to personalize their treatment. For this purpose, we utilized a hybrid combination of multi-objective optimization and support vector regression which is able to handle imbalanced datasets, select the optimal features subset and optimize the parameters of the regression model so as to maximize the predictive accuracy. The proposed method significantly overcame other state-of-the-art methods. Experimental results showed that its application can predict, with reasonable accuracy (AUC 73.8-87.2%), the outcomes of this study allowing for a precision medicine approach in treating chronic pain.