Abstract
Clinical events such as clinic visits, hospital admissions, ECG readings and lab tests are recorded in modern healthcare systems. While these offer a great wealth of information about the state of health for a patient, modeling is challenging because (a) the events are sparse and irregular; (b) reading types vary greatly between episodes; and (c) the readings do not directly tell about the underlying continuous biological and mental processes that give rise to these readings. To tackle these challenges, we propose EvSys, a deep recurrent system that disentangles the observed measurement processes from latent health processes. With this design, the model is native to arbitrarily sparse and irregular clinical measurement events, and it captures the interacting underlying health processes that are not directly observed. We validate EvSys on two public datasets, namely PhysioNet 2012 and MIMIC-III, and demonstrate that the proposed model performs favorably against state-of-the-art methods.
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Nguyen, D., Nguyen, P., Tran, T. (2022). EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_19
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DOI: https://doi.org/10.1007/978-3-030-93080-6_19
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