基于新型表示学习的生活方式感知个性化血液生物标志物预测
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning
AI总结：
该研究提出一种基于生活方式数据和血液生物标志物表征学习的个性化预测框架，在UK Biobank数据集上验证了其在临床诊断预测和未来血液标志物数值预测上的优势，为个性化医疗提供支撑。
摘要：
血液生物标志物是医疗服务提供者用于诊断、监测和治疗各类疾病的重要工具。当前的参考值和推荐范围通常依赖群体层面的统计数据，可能无法充分考虑由生活方式、遗传等因素导致的个体间差异带来的影响。本研究提出了一种新型框架，用于预测未来血液生物标志物数值，并通过从生活方式数据（身体活动与睡眠）及血液生物标志物中学习到的表征定义个性化参考范围。我们提出的方法学习了一个基于相似性的嵌入空间，该空间能够捕捉生物标志物与生活方式因素之间的复杂关系。基于UK Biobank（英国生物银行，25.7万名参与者）的实验结果表明，我们的深度学习嵌入在预测临床诊断方面优于传统方法及当前最先进的表示学习技术。我们使用UK Biobank中包含6440名有随访记录参与者的子集进行验证，结果显示，将这些嵌入和生活方式因素直接纳入血液生物标志物模型，可提升基于单次实验室检测结果对未来实验室数值的预测效果。这种个性化建模方法为开发更精准的风险分层工具和定制预防保健策略奠定了基础。在临床场景中，这意味着有望实现更早的疾病检测、更及时的干预，并最终向个性化医疗转型。
英文摘要：
Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges often rely on population-level statistics, which may not adequately account for the influence of inter-individual variability driven by factors such as lifestyle and genetics. In this work, we introduce a novel framework for predicting future blood biomarker values and define personalized references through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors. Using the UK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and current state-of-the-art representation learning techniques in predicting clinical diagnosis. Using a subset of UK Biobank of 6440 participants who have follow-up visits, we validate that the inclusion of these embeddings and lifestyle factors directly in blood biomarker models improves the prediction of future lab values from a single lab visit. This personalized modeling approach provides a foundation for developing more accurate risk stratification tools and tailoring preventative care strategies. In clinical settings, this translates to the potential for earlier disease detection, more timely interventions, and ultimately, a shift towards personalized healthcare.
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