Pankhuri Singhal, Lindsay Guare, Colleen Morse, Anastasia Lucas, Marta Byrska-Bishop, Marie A Guerraty, Dokyoon Kim, Marylyn D Ritchie, Anurag Verma
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 specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome...
2023: AMIA Summits on Translational Science Proceedings