Uncertainty Quantification on Clinical Trial Outcome Prediction

The importance of uncertainty quantification is increasingly recognized inthe diverse field of machine learning. Accurately assessing model predictionuncertainty can help provide deeper understanding and confidence forresearchers and practitioners. This is especially critical in medical diagnosisand drug discovery areas, where reliable predictions directly impact researchquality and patient health. In this paper, we proposed incorporating uncertainty quantification intoclinical trial outcome predictions. Our main goal is to enhance the model’sability to discern nuanced differences, thereby significantly improving itsoverall 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. Selectiveclassification, encompassing a spectrum of methods for uncertaintyquantification, empowers the model to withhold decision-making in the face ofsamples marked by ambiguity or low confidence, thereby amplifying the accuracyof predictions for the instances it chooses to classify. A series ofcomprehensive experiments demonstrate that incorporating selectiveclassification into clinical trial predictions markedly enhances the model’sperformance, as evidenced by significant upticks in pivotal metrics such asPR-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, andIII trial outcome prediction, respectively. When predicting phase III, ourmethod reaches 0.9022 PR-AUC scores. These findings illustrate the robustness and prospective utility of thisstrategy within the area of clinical trial predictions, potentially setting anew benchmark in the field.

Further reading#

• Access Paper in arXiv.org