Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence
This work addresses the challenge for non-experts to verify medical claims on social media, though it is incremental as it builds on existing tasks with a new dataset and method.
The research tackled the problem of aligning medical claims from social media with medical evidence by developing a system that identifies claims, extracts vocabulary, and retrieves relevant evidence, resulting in improved metrics through a novel synthetic dataset.
Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with this medical evidence. By doing so, individuals without medical expertise can more effectively assess the veracity of such medical claims. We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks. We additionally introduce a novel dataset produced by our synthetic generator that, when applied to these tasks, demonstrates not only a more flexible and holistic approach, but also an improvement in all comparable metrics. We make our dataset, the Expansive Medical Claim Corpus (EMCC), available at https://zenodo.org/records/8321460