Novel Representation Learning Improves Personalizing Blood Test Ranges and Disease Risk Prediction

Naghmeh Rezaei
Xavi Prieto
Shwetak Patel


Blood tests are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions; however, quantitative approaches for personalizing such metrics are nascent and often ignore important factors such as lifestyle. Moreover, recent studies have shown that raw (untransformed) representations of health records are inadequate for constructing predictive models, especially when considering a single timepoint. In this work, we investigate the association of activity and sleep with blood test ranges, and based on our results, propose Proteus, a new deep metric learning algorithm that accounts for lifestyle. We show that Proteus significantly improves the performance of several downstream analyses, including the prediction of future health risk in currently-healthy patients using a single laboratory visit. Building upon our findings, we additionally introduce DeepRange, a novel lifestyle-informed algorithm which utilizes deep-learned embeddings for estimating personalized optimal blood test ranges. Our proposed methodology for personalized blood test ranges and single-visit health risk prediction can be readily implemented and has the potential to significantly improve health outcomes by enabling early intervention and personalized treatment.