  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.
