标题： 使用大型语言模型从异构数据中灵活预测心血管疾病风险 显示英文标题

标题： Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models

摘要： 心血管疾病（CVD）风险预测模型对于识别高危个体和指导预防措施至关重要。然而，现有的模型难以应对真实临床实践中的挑战，因为它们过度简化患者特征，依赖固定的输入模式，并且对分布变化敏感。 我们开发了AdaCVD，这是一个基于大型语言模型的可适应性CVD风险预测框架，该模型经过超过50万名英国生物银行参与者的广泛微调。 在基准比较中， AdaCVD超越了现有的风险评分和标准机器学习方法，达到了最先进的性能。 关键的是，首次解决了临床领域三个维度的关键挑战：它灵活地整合了全面但多变的患者信息；它无缝集成结构化数据和非结构化文本；并且它使用最少的额外数据快速适应新的患者群体。 在分层分析中，它在人口统计学、社会经济和临床亚组中展示了强大的性能，包括代表性不足的队列。 AdaCVD为更灵活、以AI驱动的临床决策支持工具提供了一条有前景的道路，适合于异质性和动态医疗环境的现实需求。 显示英文摘要

摘要： Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the challenges of real-world clinical practice as they oversimplify patient profiles, rely on rigid input schemas, and are sensitive to distribution shifts. We developed AdaCVD, an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank. In benchmark comparisons, AdaCVD surpasses established risk scores and standard machine learning approaches, achieving state-of-the-art performance. Crucially, for the first time, it addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data. In stratified analyses, it demonstrates robust performance across demographic, socioeconomic, and clinical subgroups, including underrepresented cohorts. AdaCVD offers a promising path toward more flexible, AI-driven clinical decision support tools suited to the realities of heterogeneous and dynamic healthcare environments.