TY - GEN
T1 - COP-E-CAT
T2 - 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021
AU - Mandyam, Aishwarya
AU - Yoo, Elizabeth C.
AU - Soules, Jeff
AU - Laudanski, Krzysztof
AU - Engelhardt, Barbara E.
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/1/18
Y1 - 2021/1/18
N2 - In order to ensure that analyses of complex electronic healthcare record (EHR) data are reproducible and generalizable, it is crucial for researchers to use comparable preprocessing, filtering, and imputation strategies. We introduce COP-E-CAT: Cleaning and Organization Pipeline for EHR Computational and Analytic Tasks, an open-source processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset. COP-E-CAT allows users to select filtering characteristics and preprocess covariates to generate data structures for use in downstream analysis tasks. This user-friendly approach shows promise in facilitating reproducibility and comparability among studies that leverage the MIMIC-IV data, and enhances EHR accessibility to a wider spectrum of researchers than current data processing methods. We demonstrate the versatility of our workflow by describing three use cases: ensemble prediction, reinforcement learning, and dimension reduction. The software is available at: https://github.com/eyeshoe/cop-e-cat.
AB - In order to ensure that analyses of complex electronic healthcare record (EHR) data are reproducible and generalizable, it is crucial for researchers to use comparable preprocessing, filtering, and imputation strategies. We introduce COP-E-CAT: Cleaning and Organization Pipeline for EHR Computational and Analytic Tasks, an open-source processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset. COP-E-CAT allows users to select filtering characteristics and preprocess covariates to generate data structures for use in downstream analysis tasks. This user-friendly approach shows promise in facilitating reproducibility and comparability among studies that leverage the MIMIC-IV data, and enhances EHR accessibility to a wider spectrum of researchers than current data processing methods. We demonstrate the versatility of our workflow by describing three use cases: ensemble prediction, reinforcement learning, and dimension reduction. The software is available at: https://github.com/eyeshoe/cop-e-cat.
KW - electronic health records
KW - health informatics
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85112382273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112382273&partnerID=8YFLogxK
U2 - 10.1145/3459930.3469536
DO - 10.1145/3459930.3469536
M3 - Conference contribution
AN - SCOPUS:85112382273
T3 - Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021
BT - Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021
PB - Association for Computing Machinery, Inc
Y2 - 1 August 2021 through 4 August 2021
ER -