Towards Automated Detection of Contradictory Research Claims in Medical Literature Using Deep Learning Approach

In Evidence-Based Medicine (EBM), medical literature is an essential resource used by clinicians and researchers. It contains research claims that summarize the critical findings of a study. However, research claims on the same topic can be contradicting. Given a clinical question, if two claims that answer the question have conflicting assertion values (Yes or No), they are considered contradictory claims. Hence, discovering the claim assertion value of a research claim is the key to detecting contradictory research claims in medical literature. In this study, we explored the usage of deep neural network (DNN) to recognize contradictory research claims in medical literature. The DNN model should determine the assertion value of a research claim against its clinical question. The model was evaluated using a publicly available corpus containing contradictory research claims from 24 systematic reviews on cardiovascular topic. Different DNN techniques such as the Global Vectors for Word Representation (GLoVe), bidirectional Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) were implemented in building the claim assertion model. The evaluation results suggest that the BERT model performs better than LSTM and GloVe model and outperforms the previous studies’ models.