Identifying who has long COVID in the USA: a machine learning approach using N3C data.

Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA, N3C Consortium

Open source

DOI: 10.1016/s2589-7500(22)00048-6
Published: 2022 Jul
Container: The Lancet. Digital health
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1016/s2589-7500-22-00048-6,
  title = {Identifying who has long COVID in the USA: a machine learning approach using N3C data.},
  author = {Pfaff ER and Girvin AT and Bennett TD and Bhatia A and Brooks IM and Deer RR and Dekermanjian JP and Jolley SE and Kahn MG and Kostka K and McMurry JA and Moffitt R and Walden A and Chute CG and Haendel MA and N3C Consortium},
  year = {2022},
  journal = {The Lancet. Digital health},
  doi = {10.1016/s2589-7500(22)00048-6},
  url = {https://doi.org/10.1016/s2589-7500(22)00048-6}
}

RIS

TY  - JOUR
TI  - Identifying who has long COVID in the USA: a machine learning approach using N3C data.
AU  - Pfaff ER
AU  - Girvin AT
AU  - Bennett TD
AU  - Bhatia A
AU  - Brooks IM
AU  - Deer RR
AU  - Dekermanjian JP
AU  - Jolley SE
AU  - Kahn MG
AU  - Kostka K
AU  - McMurry JA
AU  - Moffitt R
AU  - Walden A
AU  - Chute CG
AU  - Haendel MA
AU  - N3C Consortium
PY  - 2022
JO  - The Lancet. Digital health
DO  - 10.1016/s2589-7500(22)00048-6
UR  - https://doi.org/10.1016/s2589-7500(22)00048-6
ER  -

APA

ER, P., AT, G., TD, B., A, B., IM, B., RR, D., JP, D., SE, J., MG, K., K, K., JA, M., R, M., A, W., CG, C., MA, H., & Consortium, N. (2022). Identifying who has long COVID in the USA: a machine learning approach using N3C data.. The Lancet. Digital health. https://doi.org/10.1016/s2589-7500(22)00048-6

Source records

pubmed · retrieved 2026-09-26T16:16:37.011Z