Abstract
Electronic Health Records (EHRs) have been widely used in healthcare studies recently, such as the analyses for patient diagnostic outcome and understanding of disease progression. EHR is a treasure for researchers who conduct the Real-World study to discovering Real-World Evidence (RWE). In this paper, we design an end-to-end learning system for disease states discovery based on a data-driven strategy. A large-scale proprietary EHR data mart containing about 55 million patients with over 20 billion data records is used for data extraction and analysis. Given a disease of interest, researchers could easily obtain the hidden disease states. Once our system were operational, biomedical researchers could get the results for downstream analyses such as disease prediction, drug design and outcome analyses.
Z. Tang and L. Hu—Contributed equally.
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Tang, Z. et al. (2020). Embracing Disease Progression with a Learning System for Real World Evidence Discovery. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_46
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DOI: https://doi.org/10.1007/978-3-030-60802-6_46
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