Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drug–test pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients’ history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation.

标准化的实验室测试是患者评估、鉴别诊断和治疗的核心。然而，对这些数据的解释缺乏定量和个性化的指标。在这里，我们报告了 18 年间 280 万成年人对 92 项不同实验室测试的 21 亿次实验室测量结果的建模。在对 131 种慢性病和 5,223 对药物测试对进行无监督过滤后，我们对健康个体的实验室测试分布进行了虚拟调查。在 92 次测试中的 89 次中，仅年龄和性别解释了不到 10% 的正常测试方差。基于患者病史的个性化模型解释了 17 次测试的 60% 和一半测试的 36% 以上。这允许对未来异常测试水平和随后出现的疾病的风险进行系统分层。