Journal Article

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Fei, Zhihui, Li, Min, Yu, Ying, Pan, Yi, Wu, Fang-Xiang, Liu, Liangliang, Li, Hong-Dong, Wang, Jianxin

2018

Overview

Background Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model. Results In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients. Conclusion Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

• Publisher: BioMed Central,BioMed Central Ltd
• Subject: Algorithms / Analgesics / Arthritis / Artificial intelligence / Biocompatibility / Bioinformatics / Biomedical and Life Sciences / Blood / Cardiovascular disease / Cardiovascular diseases / Care and treatment / Cellular and Medical Topics / Complications and side effects / Computational Biology/Bioinformatics / Data mining / Decision making / Electronic health records / Electronic medical records / Family medical history / Health risks / Heart diseases / International conferences / Knee / Learning algorithms / Life Sciences / Machine learning / Mathematical models / Medical research / Model accuracy / Nonsteroidal anti-inflammatory drugs / Osteoarthritis / Pain / Patients / Physiological / Prediction models / Researchers / Risk / Side effects / Simulation and Modeling / Studies / Systems Biology