An interpretable model for predicting side effects of analgesics for osteoarthritis

Osteoarthritis (OA) is the most common type of arthritis. Analgesics are widely used in the process of the treatment of arthritis. Analgesics are particularly used by OA patients which may increase the risk of cardiovascular disease by 20% to 50% overall. In this study, we proposed an interpretable model to predict side effects of analgesics on cardiovascular disease for OA patients. One task of our study is to predict whether OA patients can use analgesics. We weighed accuracy and interpretability among state-of-the-art methods, and constructed a non-linear model by the Gradient Boosting Decision Tree technique. The AUC of the prediction model was 0.96. Another task was to select informative risk features (RFs) by our proposed model. We sought to identify risk features in literature from the biomedical. Most of the selected RFs are validated by the medical literature and some new RFs could attract the interest across the medical research. The performance of the proposed model, showed its superiority compared with well-known machine learning algorithms in terms of AUC.

I. Introduction

Osteoarthritis (OA) is the most common joint disease for adults worldwide [1]. By 2020, OA is predicted to become the fourth leading cause of disability globally [2]. Standard treatments usually begin with non-pharmacologic approaches for symptom relief and functional recovery, including weight loss, orthotic devices, diet, exercise and physical therapy. Since these treatments usually provide limited pain relief, analgesics are widely used during the treatment of OA.