Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning

Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Establishing personalized blood biomarker ranges is crucial for accurate dis-ease diagnosis and management. Current clinical ranges often rely on population-level statistics, which may not adequately account for the substantial 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 personalized reference ranges through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that aims to capture the complex relationship between biomarkers and lifestyle factors. UsingUK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and cur-rent 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.