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
this https URL

通过将社交媒体平台上的医疗主张与最新的、最佳的证据相结合，本研究旨在帮助非医学专业人士更有效地评估这些医疗主张的真实性。我们研究了三个核心任务：识别医疗主张、提取医学词汇以及检索与这些医疗主张相关的证据，并提出了一个能够为每个核心任务生成合成医疗主张的新型系统。此外，我们引入了一个由合成生成器产生的新型数据集，应用于这些任务时，不仅展示了更灵活、更全面的方法，而且在所有可比指标上都有改进。我们将我们的数据集 ——Expansive Medical Claim Corpus (EMCC)—— 提供给大家，网址为此 https URL。