AutoDiscern: rating the quality of online health information with hierarchical encoder attention-based neural networks

BMC Medical Informatics and Decision Making
Laura KinkeadMichael Krauthammer

Abstract

Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address this, the DISCERN criteria (developed at University of Oxford) are used to evaluate the quality of online health information. However, patients are unlikely to take the time to apply these criteria to the health websites they visit. We built an automated implementation of the DISCERN instrument (Brief version) using machine learning models. We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores across all criteria of 0.75 and 0.74, respectively, outperforming the Random Forest model (average F1-macro = 0.69). Overall, the neural network based models achieved 81% and 86% average accuracy at 100% and 80% coverage, respectively, compared to 94% manual rating accuracy. The attention mechanism implemented in the HEA arch...Continue Reading

References

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Oct 7, 2016·Journal of the American Medical Informatics Association : JAMIA·Ahmed AllamMichael Krauthammer
Jan 29, 2017·Journal of the American Medical Informatics Association : JAMIA·Dina Demner-FushmanAlan R Aronson

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Citations

Jan 5, 2021·Frontiers in Public Health·Majed Al-JefriPietro Ghezzi
Aug 9, 2021·European Journal of Obstetrics, Gynecology, and Reproductive Biology·Mariana Furtado MeinbergAgnaldo Lopes da Silva-Filho

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Software Mentioned

BioBert
HEA
MetaMap Lite
HEA BioBert
qquad
DISCERN
HE BioBERT
BERT
learn
PyTorch

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