DETECT: Feature extraction method for disease trajectory modeling in electronic health records

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 DiseasE Trajectory fEature extraCTion (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. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset 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.

AMIA Jt Summits Transl Sci Proc. 2023 Jun 16;2023:487-496. eCollection 2023.

PMID: 37350926 PMCID: PMC10283148


Other Contributors

Pankhuri Singhal 1, Lindsay Guare 1, Colleen Morse 2, Anastasia Lucas 1, Marta Byrska-Bishop 3, Marie A Guerraty 2, Dokyoon Kim 4 5, Marylyn D Ritchie 1 5 6, Anurag Verma 2 7

1Department of Genetics, University of Pennsylvania, Philadelphia, PA.
2Department of Medicine, University of Pennsylvania, Philadelphia, PA.
3New York Genome Center, New York, NY.
4Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
5Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA.
6Center for Precision Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
7Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA.