Ali Heydari
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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.
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We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly regularize the distance between the positive and negative samples in a triplet with respect to the anchor-negative distance. As an initial validation, we show that our method (called No Pairs Left Behind [NPLB]) improves upon the traditional and current state-of-the-art triplet objective formulations on standard benchmark datasets. To show the effectiveness and potentials of NPLB on real-world complex data, we evaluate our approach on a large-scale healthcare dataset (UK Biobank), demonstrating that the embeddings learned by our model significantly outperform all other current representations on tested downstream tasks. Additionally, we provide a new model-agnostic single-time health risk definition that, when used in tandem with the learned representations, achieves the most accurate prediction of subjects' future health complications. Our results indicate that NPLB is a simple, yet effective framework for improving existing deep metric learning models, showcasing the potential implications of metric learning in more complex applications, especially in the biological and healthcare domains.
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