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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: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.
Autores principais:Aparício, Carolina
Assunto:Clinical trial outcomes Large Language Models Mixture-of-Experts LIFTED Retrieval-Augmented-Generation SHAP
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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