AutoDiscern: rating the quality of online health information with hierarchical encoder attention-based neural networks
When facing health problems, patients increasingly turn to search engines and online information before or instead of talking to their doctor. However, as online health information is often of poor quality, this practice leads to potential misinformation. In this study, various machine learning models were built to evaluate the quality of online health information using the DISCERN criteria, which were developed at University of Oxford. The results suggest that automating the quality assessment of online health information is feasible, representing an important step towards enabling patients to become informed partners in the health process.