Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning

TL;DR

This paper presents a novel deep learning framework that uses lifestyle data to create personalized blood biomarker reference models, improving future biomarker prediction and enabling more tailored healthcare interventions.

Contribution

The study introduces a new representation learning approach that incorporates lifestyle factors to personalize blood biomarker predictions, outperforming existing methods.

Findings

Deep embeddings outperform traditional models in clinical diagnosis prediction.

Including lifestyle data improves future blood biomarker value predictions.

Personalized models enable earlier disease detection and tailored healthcare.

Abstract

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.