  Clinical trials are essential to drug development but time-consuming, costly,
and prone to failure. Accurate trial outcome prediction based on historical
trial data promises better trial investment decisions and more trial success.
Existing trial outcome prediction models were not designed to model the
relations among similar trials, capture the progression of features and designs
of similar trials, or address the skewness of trial data which causes inferior
performance for less common trials.
  To fill the gap and provide accurate trial outcome prediction, we propose
Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first
identifies trial topics to cluster the multi-sourced trial data into relevant
trial topics. It then generates trial embeddings and organizes them by topic
and time to create clinical trial sequences. With the consideration of each
trial sequence as a task, it uses a meta-learning strategy to achieve a point
where the model can rapidly adapt to new tasks with minimal updates. In
particular, the topic discovery module enables a deeper understanding of the
underlying structure of the data, while sequential learning captures the
evolution of trial designs and outcomes. This results in predictions that are
not only more accurate but also more interpretable, taking into account the
temporal patterns and unique characteristics of each trial topic. We
demonstrate that SPOT wins over the prior methods by a significant margin on
trial outcome benchmark data: with a 21.5\% lift on phase I, an 8.9\% lift on
phase II, and a 5.5\% lift on phase III trials in the metric of the area under
precision-recall curve (PR-AUC).
