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EGEMS (Washington, DC)|August 6, 2019
Using Electronic Medical Records to Identify Enhanced Recovery After Surgery CasesNikki L B Freeman, Katharine L McGinigle, Peter J Leese
Medrxiv : the Preprint Server for Health Sciences|October 3, 2025
Epidemiologic Method Review at Scale: Assessing Charlson Comorbidity Versioning Using a Large Language ModelJoshua T Fuchs, Cara Johnson, Nathan Foster, et al.
American Journal of Hypertension|March 27, 2010
Cost-effectiveness of electrocardiography vs. electrocardiography plus limited echocardiography to diagnose LVH in young, newly identified, hypertensivesPeter J Leese, Anthony J Viera, Alan L Hinderliter, et al.
Journal of Occupational and Environmental Medicine|December 3, 2009
A longitudinal study on the relationship between weight loss, medical expenditures, and absenteeism among overweight employees in the WAY to Health studyEric A Finkelstein, Laura A Linnan, Deborah F Tate, et al.
Journal of the American Medical Informatics Association : JAMIA|April 15, 2025
Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage processJohn P Powers, Samyuktha Nandhakumar, Sofia Z Dard, et al.
The Journal of Surgical Research|April 23, 2026
Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease ResearchPeter J Leese, Tomas McIntee, Sydney E Browder, et al.
Communications Medicine|April 10, 2025
Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohortJohn P Powers, Tomas J McIntee, Abhishek Bhatia, et al.
Medrxiv : the Preprint Server for Health Sciences|October 14, 2022
Long COVID Risk and Pre-COVID Vaccination: An EHR-Based Cohort Study from the RECOVER ProgramM Daniel Brannock, Robert F Chew, Alexander J Preiss, et al.
Nature Communications|May 22, 2023
Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER programM Daniel Brannock, Robert F Chew, Alexander J Preiss, et al.
Medrxiv : the Preprint Server for Health Sciences|July 10, 2026
Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer typesJoshua T Fuchs, Matthew J Satusky, Peter J Leese, et al.
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