Computable Phenotypes for Long-COVID in EHR data

Industrial Problems Seminar 

Miles Crosskey (CoVar Applied Technologies)


Long COVID, a condition characterized by persistent symptoms following COVID-19 infection, poses challenges in identification due to its diverse manifestations and novelty. Leveraging the N3C Enclave's electronic health record (EHR) data, we devised a machine learning method to construct a computable phenotype for Long COVID. This approach enables the identification of individuals with this condition through EHR data. Our model demonstrates a sensitivity of 72.7% and a specificity of 96.3%, maintaining consistent performance on held-out sites. This technique contributes to a better understanding of Long COVID's prevalence and impact.

Start date
Friday, Sept. 15, 2023, 1:25 p.m.
End date
Friday, Sept. 15, 2023, 2:25 p.m.

Lind Hall 325 or Zoom

Zoom registration