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

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: 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.

Authors

  • Laura Kinkead
    Department of Quantitative Biomedicine, UZH, Schmelzbergstrasse 26, Zurich, Switzerland.
  • Ahmed Allam
    Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Michael Krauthammer
    Yale School of Medicine, New Haven, CT.