  The importance of uncertainty quantification is increasingly recognized in
the diverse field of machine learning. Accurately assessing model prediction
uncertainty can help provide deeper understanding and confidence for
researchers and practitioners. This is especially critical in medical diagnosis
and drug discovery areas, where reliable predictions directly impact research
quality and patient health.
  In this paper, we proposed incorporating uncertainty quantification into
clinical trial outcome predictions. Our main goal is to enhance the model's
ability to discern nuanced differences, thereby significantly improving its
overall performance.
  We have adopted a selective classification approach to fulfill our objective,
integrating it seamlessly with the Hierarchical Interaction Network (HINT),
which is at the forefront of clinical trial prediction modeling. Selective
classification, encompassing a spectrum of methods for uncertainty
quantification, empowers the model to withhold decision-making in the face of
samples marked by ambiguity or low confidence, thereby amplifying the accuracy
of predictions for the instances it chooses to classify. A series of
comprehensive experiments demonstrate that incorporating selective
classification into clinical trial predictions markedly enhances the model's
performance, as evidenced by significant upticks in pivotal metrics such as
PR-AUC, F1, ROC-AUC, and overall accuracy.
  Specifically, the proposed method achieved 32.37\%, 21.43\%, and 13.27\%
relative improvement on PR-AUC over the base model (HINT) in phase I, II, and
III trial outcome prediction, respectively. When predicting phase III, our
method reaches 0.9022 PR-AUC scores.
  These findings illustrate the robustness and prospective utility of this
strategy within the area of clinical trial predictions, potentially setting a
new benchmark in the field.
