用临床与数字数据融合预测多发性硬化症早期恶化，提升预判准确率。
Early Prediction of Multiple Sclerosis Disability Progression via Multimodal Foundation Model Benchmarks
- 融合临床与每日数字追踪数据，用基础模型建模时间序列特征。
- 72周预测的AUROC达0.63，数字数据显著提升模型性能。
- 适合神经病学、数字健康研究者参考，推动慢病精准预测。
多发性硬化症（MS）病情异质性强，早期残疾进展预测极具挑战。本研究基于CONSONANCE临床试验中稀疏基线临床数据与12周每日数字Floodlight数据，预测48周和72周后的残疾程度。采用先进的表格与时间序列基础模型（FMs）、定制的多模态注意力Transformer及传统机器学习方法。尽管早期预测难度大（AUROC 0.63），但引入数字数据后，模型表现优于仅使用临床数据。使用Moment FM生成的单模态嵌入构建的Transformer取得最佳效果，而我们的多模态Transformer始终优于其单模态版本，验证了临床与数字数据融合的优势。研究结果表明，基础模型与多模态方法能从复杂多样的生命科学数据（如影像、组学）中提取预测信号，为MS及其他复杂疾病提供更精准的预后评估。
原文摘要 · Abstract (English)
Early multiple sclerosis (MS) disability progression prediction is challenging due to disease heterogeneity. This work predicts 48- and 72-week disability using sparse baseline clinical data and 12 weeks of daily digital Floodlight data from the CONSONANCE clinical trial. We employed state-of-the-art tabular and time-series foundation models (FMs), a custom multimodal attention-based transformer, and machine learning methods. Despite the difficulty of early prediction (AUROC 0.63), integrating digital data via advanced models improved performance over clinical data alone. A transformer model using unimodal embeddings from the Moment FM yielded the best result, but our multimodal transformer consistently outperformed its unimodal counterpart, confirming the advantages of combining clinical with digital data. Our findings demonstrate the promise of FMs and multimodal approaches to extract predictive signals from complex and diverse clinical and digital life sciences data (e.g., imaging, omics), enabling more accurate prognostics for MS and potentially other complex diseases.
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