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- W4309227547 abstract "Abstract Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm D iseas E T rajectory f E ature extra CT ion ( DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between a specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes." @default.
- W4309227547 created "2022-11-24" @default.
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- W4309227547 date "2022-11-13" @default.
- W4309227547 modified "2023-09-26" @default.
- W4309227547 title "DETECT: Feature extraction method for disease trajectory modeling" @default.
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- W4309227547 doi "https://doi.org/10.1101/2022.11.06.22281817" @default.
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