Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction.

Area of Science:

• * Computational biology and bioinformatics
• * Machine learning in healthcare
• * Pharmaceutical research and development

Background:

• * Clinical trials are essential but costly and time-consuming for new therapy development.
• * Accurate clinical trial approval prediction can optimize resource allocation by identifying trials likely to fail.
• * Existing prediction models lack uncertainty quantification and interpretability, limiting practical application.

Purpose of the Study:

• * To quantify uncertainty and enhance interpretability in clinical trial approval predictions.
• * To improve the reliability and practical utility of predictive models in clinical trial management.
• * To enable better decision-making in the allocation of resources for drug development.

Main Methods:

• * Integration of a selective classification approach with the Hierarchical Interaction Network model.
• * Utilization of uncertainty quantification methods to enable models to abstain from low-confidence predictions.
• * Development of a model that enhances prediction accuracy and provides interpretability.

Main Results:

• * Significant improvements in Area Under the Precision-Recall Curve (AUPRC) across trial phases: 32.37% (Phase I), 21.43% (Phase II), and 13.27% (Phase III).
• * Achieved an AUPRC score of 0.9022 for Phase III trial approval predictions.
• * Demonstrated enhanced model interpretability through a case study, aiding domain expert understanding.

Conclusions:

• * The proposed approach effectively measures model uncertainty in clinical trial outcome prediction.
• * Incorporating uncertainty quantification significantly improves prediction performance and interpretability.
• * This method offers a valuable tool for optimizing clinical trial management and drug development processes.