Personalized lab test models to quantify disease potentials in healthy individuals

A new approach based on machine-learning integration of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults, over a span of 18 years, produces models that can stratify one's risk of having a future abnormal lab test level and subsequent emerging disease. 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.

Automated summary

A new approach based on machine learning integrates a large amount of laboratory test data to stratify and predict the risk of future diseases, providing a multivariate model for quantitative patient evaluation.