NORMA解决了实验室检测中过度个性化导致的假阳性问题，做临床诊断或健康监测的医生、研究者可以直接用这个开源模型来提升异常检测的准确性。
血液生物标志物是临床诊断的关键，但传统参考区间基于固定人群，忽略了个体间稳定变异，可能掩盖偏离基线的异常。研究者利用北美、中东和东亚超过160万人的近20亿条纵向实验室数据发现，纯个性化参考区间会将高达68%的测量值标记为异常，且与不良临床结局无关。为此，他们提出NORMA，一个基于条件Transformer的框架，通过结合患者历史数据和人群正常变异生成参考区间。NORMA在预测死亡率、急性肾损伤和慢性疾病等结局上精度更高，优于纯个性化或纯人群方法。模型、代码和交互界面已公开，旨在推动实验室检测的个性化解释。
Learning Normal Representations for Blood Biomarkers
Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient variability. As such, population-based interpretation can mask meaningful deviation from an individual's baseline, risking delayed disease detection. To remedy this, there have been increasing efforts to personalize blood biomarker interpretation using individual testing histories. However, these methods may overfit to sparse data, inflating false-positive rates and unnecessary follow-up, and can also unwittingly include unrecognized or subclinical disease. Here, we leverage nearly 2 billion longitudinal laboratory measurements from over 1.6 million individuals across North America, the Middle East, and East Asia, to show that while laboratory values are highly individual, purely personalized intervals routinely overfit, classifying up to 68% of measurements as abnormal, without corresponding associations with adverse clinical outcomes. We then introduce NORMA, a conditional transformer-based framework that generates reference intervals by conditioning on both a patient's history and population-level data about "normal" variation. NORMA-derived intervals achieve higher precision for predicting outcomes, including mortality, acute kidney injury, and chronic disease. These findings caution against over-personalization in laboratory medicine and demonstrate that anchoring individual trajectories to population-level priors outperforms either approach alone. To promote transparency, we publicly release the model, code, and an interactive user interface for accessible, individualized laboratory interpretation.