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

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 2001-2018 (Commu...

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Detalles Bibliográficos
Autores principales: Khurshid, Shaan, Reeder, Christopher, Harrington, Lia X., Singh, Pulkit, Sarma, Gopal, Friedman, Samuel F., Di Achille, Paolo, Diamant, Nathaniel, Cunningham, Jonathan W., Turner, Ashby C., Lau, Emily S., Haimovich, Julian S., Al-Alusi, Mostafa A., Wang, Xin, Klarqvist, Marcus D. R., Ashburner, Jeffrey M., Diedrich, Christian, Ghadessi, Mercedeh, Mielke, Johanna, Eilken, Hanna M., McElhinney, Alice, Derix, Andrea, Atlas, Steven J., Ellinor, Patrick T., Philippakis, Anthony A., Anderson, Christopher D., Ho, Jennifer E., Batra, Puneet, Lubitz, Steven A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993873/
https://www.ncbi.nlm.nih.gov/pubmed/35396454
http://dx.doi.org/10.1038/s41746-022-00590-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993873/
https://www.ncbi.nlm.nih.gov/pubmed/35396454
http://dx.doi.org/10.1038/s41746-022-00590-0

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