Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug–Drug Interaction Information Retrieval - Xie, Weixin; Wang, Limei; Cheng, Qi; Wang, Xueying; Wang, Ying; Bi, Hongyuan; He, Bo; Feng, Weixing (Frontiers in Pharmacology, 2021)
Clinical drug-drug interactions (DDIs) have been a major cause for not only medical error but also adverse drug events. The published literature on DDI clinical toxicity continues to grow significantly, and high-performance DDI information retrieval (IR) text mining methods are in high demand. The effectiveness of IR and its machine learning (ML) algorithm depends on the availability of a large amount of training and validation data that have been manually reviewed and annotated. In this study, we investigated how active learning (AL) might improve ML performance in DDI IR analysis. We recognized that a direct application of AL would not address several primary challenges in DDI IR from the literature. For instance, the vast majority of abstracts in PubMed will be negative, existing positive and negative labeled samples do not represent the general sample distributions, and potentially biased samples may arise during uncertainty sampling in an AL algorithm. Therefore, we developed several novel sampling and ML schemes to improve AL performance in DDI IR analysis. In particular, random negative sampling was added as a part of AL. We also used two ML algorithms in an AL process to differentiate random negative samples from manually labeled negative samples, and updated both the training and validations samples during the AL process to avoid or reduce biased sampling. Two supervised ML algorithms, support vector machine (SVM) and logistic regression (LR) were used to investigate the consistency of our proposed AL algorithm. Because the ultimate goal of DDI IR is to retrieve all DDI toxicity relevant abstracts, a recall rate of 0.99 was set in developing the AL methods. When we used our newly proposed AL method with SVM, the precision in differentiating the positive samples from manually labeled negative samples improved from 0.45 in the first round to 0.83 in the second round; and the precision in differentiating the positive samples from random negative samples improved from 0.72 to 0.82 in the first and second rounds respectively. When our proposed AL method was used with LR, the improvements in precision followed a similar trend.