https://www.selleckchem.com/products/sel120.html
High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs. We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records the aggregated vector of the records (agg
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