  Clinical trials provide essential guidance for practicing Evidence-Based
Medicine, though often accompanying with unendurable costs and risks. To
optimize the design of clinical trials, we introduce a novel Clinical Trial
Result Prediction (CTRP) task. In the CTRP framework, a model takes a
PICO-formatted clinical trial proposal with its background as input and
predicts the result, i.e. how the Intervention group compares with the
Comparison group in terms of the measured Outcome in the studied Population.
While structured clinical evidence is prohibitively expensive for manual
collection, we exploit large-scale unstructured sentences from medical
literature that implicitly contain PICOs and results as evidence. Specifically,
we pre-train a model to predict the disentangled results from such implicit
evidence and fine-tune the model with limited data on the downstream datasets.
Experiments on the benchmark Evidence Integration dataset show that the
proposed model outperforms the baselines by large margins, e.g., with a 10.7%
relative gain over BioBERT in macro-F1. Moreover, the performance improvement
is also validated on another dataset composed of clinical trials related to
COVID-19.
