Cohort design and natural language processing to reduce bias in electronic health records research

Cohort design and natural language processing to reduce bias in electronic health records research

Abstract Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-20...

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Bibliographic Details
Main Authors: Shaan Khurshid, Christopher Reeder, Lia X. Harrington, Pulkit Singh, Gopal Sarma, Samuel F. Friedman, Paolo Di Achille, Nathaniel Diamant, Jonathan W. Cunningham, Ashby C. Turner, Emily S. Lau, Julian S. Haimovich, Mostafa A. Al-Alusi, Xin Wang, Marcus D. R. Klarqvist, Jeffrey M. Ashburner, Christian Diedrich, Mercedeh Ghadessi, Johanna Mielke, Hanna M. Eilken, Alice McElhinney, Andrea Derix, Steven J. Atlas, Patrick T. Ellinor, Anthony A. Philippakis, Christopher D. Anderson, Jennifer E. Ho, Puneet Batra, Steven A. Lubitz
Format: Article
Language:English
Published: Nature Portfolio 2022-04-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-022-00590-0

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https://doi.org/10.1038/s41746-022-00590-0

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