MMCTOP: A Multimodal Textualization and Mixture-of-Experts Framework for Clinical Trial Outcome Prediction

2025-12-26

AI Summary

To address the challenges of heterogeneous multimodal data integration (molecular structures, enrollment text, disease ontologies) and insufficient interpretability and calibration in clinical trial outcome prediction within high-dimensional biomedical informatics, this paper proposes a joint architecture comprising Schema-Guided Textualization and Drug-Disease-Conditioned Sparse Mixture of Experts (MoE). The former enables controllable, ontology-aware narrative standardization via schema-guided textualization; the latter achieves cross-modal alignment and subspace-specialized modeling through conditional sparse expert routing. The method integrates domain-specific encoders, fidelity verification, temperature scaling for calibration, and top-k gating—ensuring auditability, probabilistic calibration, and computational scalability. Empirical evaluation across multiple benchmarks demonstrates significant improvements in AUC, F1-score, and precision over unimodal and state-of-the-art multimodal baselines. Ablation studies confirm that the textualization strategy and conditional routing are critical drivers of both performance and stability.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Addressing the challenge of multimodal data fusion in high-dimensional biomedical informatics, we propose MMCTOP, a MultiModal Clinical-Trial Outcome Prediction framework that integrates heterogeneous biomedical signals spanning (i) molecular structure representations, (ii) protocol metadata and long-form eligibility narratives, and (iii) disease ontologies. MMCTOP couples schema-guided textualization and input-fidelity validation with modality-aware representation learning, in which domain-specific encoders generate aligned embeddings that are fused by a transformer backbone augmented with a drug-disease-conditioned sparse Mixture-of-Experts (SMoE). This design explicitly supports specialization across therapeutic and design subspaces while maintaining scalable computation through top-k routing. MMCTOP achieves consistent improvements in precision, F1, and AUC over unimodal and multimodal baselines on benchmark datasets, and ablations show that schema-guided textualization and selective expert routing contribute materially to performance and stability. We additionally apply temperature scaling to obtain calibrated probabilities, ensuring reliable risk estimation for downstream decision support. Overall, MMCTOP advances multimodal trial modeling by combining controlled narrative normalization, context-conditioned expert fusion, and operational safeguards aimed at auditability and reproducibility in biomedical informatics.

Problem

• Fuses multimodal biomedical data for outcome prediction
• Integrates molecular, protocol, and disease information
• Improves prediction accuracy and reliability in clinical trials

Innovation

• Schema-guided textualization for multimodal data integration
• Drug-disease-conditioned sparse Mixture-of-Experts for specialized fusion
• Temperature scaling for calibrated probabilities in risk estimation