Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning
<|reference_start|>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. 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.<|reference_end|>
@article{heydari2024lifestyle-informed,
    title={Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel
  Representation Learning},
    author={A. Ali Heydari, Naghmeh Rezaei, Javier L. Prieto, Shwetak N. Patel,
  Ahmed A. Metwally},
    journal={arXiv preprint arXiv:2407.07277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07277},
    primaryClass={cs.LG cs.AI}
}