Clinical trial outcome prediction using a multimodal mixture-of-experts approach expanding on the LIFTED framework and interpretation aided by SHAP explanations

Abstract

This work presents MMCTO, a multimodal framework predicting clinical trial outcomes by integrating molecular, disease, and eligibility data. Based on the LIFTED architecture, it employs natural language transformation and a Mixture-of-Experts mechanism to unify heterogeneous inputs. It demonstrates superior predictive performance across trial phases on HINT and CTOD datasets. Ablation studies confirm the importance of LLM-generated features and conditioned gating. Finally, for the individual body of work I’ll explore the SHAP explanations which aim to provide transparency. The approach optimizes resources and streamlines processes, potentially avoiding costly failures and accelerating drug development timelines.