Publicação
Clinical trial outcome prediction using a multimodal mixture-of-experts approach expanding on the LIFTED framework and interpretation aided by SHAP explanations
| 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. 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. |
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| Autores principais: | Mota, Tiago |
| Assunto: | Clinical Trial Outcomes HINT Large Language Models Mixture-of-Experts LIFTED Natural Language 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 |
| 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. 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. |
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