将基因风险评分融入电子病历模型，提升疾病预测能力。
Integrating Genomics into Multimodal EHR Foundation Models
- 把遗传风险评分作为基础数据模态，融合临床与基因信息。
- 在All of Us数据上对2型糖尿病预测效果显著提升。
- 适合关注个性化医疗与疾病风险分层的研究者使用。
本文提出一种创新的电子健康记录（EHR）基础模型，将多基因风险评分（PRS）作为核心数据模态，突破传统仅依赖EHR的局限，构建更全面的健康画像。基于美国全民研究计划（All of Us, AoU）的丰富多样数据，该多模态框架旨在学习临床数据与遗传易感性之间的复杂关系。方法上扩展生成式AI技术至EHR基础模型领域，增强了预测性能与可解释性。在AoU数据上的评估显示，该模型对多种疾病发病具有预测价值，尤其在2型糖尿病（T2D）方面表现突出，并揭示了PRS与EHR数据间的协同作用。研究还探索了迁移学习在定制分类任务中的应用，验证了架构的通用性与高效性。该方法为疾病预测、主动健康管理、风险分层及个性化治疗策略提供了新范式，有助于推动更个性化、公平且可行动的真实世界证据生成。
原文摘要 · Abstract (English)
This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.
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