Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

no code implementations • 5 Oct 2022 • Saurav Sengupta, Johanna Loomba, Suchetha Sharma, Donald E. Brown, Lorna Thorpe, Melissa A Haendel, Christopher G Chute, Stephanie Hong

Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70. 48\%.

A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

no code implementations • 13 Jun 2022 • Elena Casiraghi, Rachel Wong, Margaret Hall, Ben Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, Kenneth Wilkins, :, Tell Bennet, Christopher Chute, Peter DeWitt, Kenneth Gersing, Andrew Girvin, Melissa Haendel, Jeremy Harper, Janos Hajagos, Stephanie Hong, Emily Pfaff, Jane Reusch, Corneliu Antoniescu, Kimberly Robaski

In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques.

Imputation