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.

翻译：不确定性量化在机器学习领域的多元场景中日益受到重视。准确评估模型预测的不确定性，有助于为研究人员和实践者提供更深入的理解与信心。这在医疗诊断和药物发现领域尤为关键，因为可靠的预测直接影响研究质量与患者健康。本文提出将不确定性量化融入临床试验结果预测。我们的主要目标是增强模型辨别细微差异的能力，从而显著提升其整体性能。为实现此目标，我们采用了选择性分类方法，并将其无缝集成到临床试验预测建模前沿的层次交互网络（HINT）中。选择性分类涵盖了多种不确定性量化方法，使模型在面对模糊性或置信度较低的样本时能够暂缓决策，从而提升其决定分类的实例的预测准确性。一系列全面实验表明，将选择性分类融入临床试验预测可显著提升模型性能，PR-AUC、F1、ROC-AUC及整体准确率等关键指标均有显著增长。具体而言，在I期、II期和III期试验结果预测中，所提方法在PR-AUC指标上相较于基础模型（HINT）分别实现了32.37%、21.43%和13.27%的相对提升。在预测III期试验时，该方法达到了0.9022的PR-AUC分数。这些结果揭示了该策略在临床试验预测领域的稳健性与潜在应用价值，可能为该领域树立新的基准。