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Clinical trial outcome prediction using a multimodal mixture-of-experts approach - an interpretative framework for predicting and explaining clinical trial failures with a retrieval-augmented chatbot
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Resumo(s)
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. A RAG pipeline contextualizes results, while SHAP explanations
provide transparency. The approach optimizes resources and streamlines processes, potentially
avoiding costly failures and accelerating drug development timelines.
Descrição
Palavras-chave
Clinical trial outcomes Large Language Models Mixture-of-Experts LIFTED Retrieval-Augmented-Generation SHAP