How do we most effectively treat a disease or condition? Ideally, we could
consult a database of evidence gleaned from clinical trials to answer such
questions. Unfortunately, no such database exists; clinical trial results are
instead disseminated primarily via lengthy natural language articles. Perusing
all such articles would be prohibitively time-consuming for healthcare
practitioners; they instead tend to depend on manually compiled systematic
reviews of medical literature to inform care.
NLP may speed this process up, and eventually facilitate immediate consult of
published evidence. The Evidence Inference dataset was recently released to
facilitate research toward this end. This task entails inferring the
comparative performance of two treatments, with respect to a given outcome,
from a particular article (describing a clinical trial) and identifying
supporting evidence. For instance: Does this article report that chemotherapy
performed better than surgery for five-year survival rates of operable cancers?
In this paper, we collect additional annotations to expand the Evidence
Inference dataset by 25\%, provide stronger baseline models, systematically
inspect the errors that these make, and probe dataset quality. We also release
an abstract only (as opposed to full-texts) version of the task for rapid model
prototyping. The updated corpus, documentation, and code for new baselines and
evaluations are available at this http URL

本文讨论如何利用 NLP 技术快速分析大量的疾病治疗相关的文章，以便于有效地整理出临床试验结果，并希望以此为基础构建出一个比较不同治疗方法的数据库，方便医生进行治疗决策。同时，本文还介绍了一个被称为 Evidence Inference 的公开数据集，该数据集包括了很多描述临床试验的文献，可以用于相关研究。