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Automated Identification of Potential Conflict-of-Interest in Biomedical Articles Using Hybrid Deep Neural Network.
Conflicts-of-interest (COI) in biomedical research may cause ethical risks, including pro-industry conclusions, restrictions on the behavior of investigators, and the use of biased study designs. To ensure the impartiality and objectivity in research, many journal publishers require authors to provide a COI statement within the body text of their articles at the time of peer-review and publication. However, author’s self-reported COI disclosure often does not explicitly appear in their article, and may not be very accurate or reliable. In this study, we present a two-stage machine learning scheme using a hybrid deep learning neural network (HDNN) that combines a multi-channel convolutional neural network (CNN) and a feed-forward neural network (FNN), to automatically identify a potential COI in online biomedical articles. HDNN is designed to simultaneously learn a syntactic and semantic representation of text, relationships between neighboring words in a sentence, and handcrafted input features, and achieves a better performance overall (accuracy exceeding 96.8%) than other classifiers such as support vector machine (SVM), single/multichannel CNNs, Long Short-term Memory (LSTM), and an Ensemble model in a series of classification experiments.