  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
