CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.

Garg A, Alag S, Duncan D

Open source

DOI: 10.3390/diagnostics14030337
Published: 2024 Feb 4
Container: Diagnostics (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/diagnostics14030337,
  title = {CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.},
  author = {Garg A and Alag S and Duncan D},
  year = {2024},
  journal = {Diagnostics (Basel, Switzerland)},
  doi = {10.3390/diagnostics14030337},
  url = {https://doi.org/10.3390/diagnostics14030337}
}

RIS

TY  - JOUR
TI  - CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.
AU  - Garg A
AU  - Alag S
AU  - Duncan D
PY  - 2024
JO  - Diagnostics (Basel, Switzerland)
DO  - 10.3390/diagnostics14030337
UR  - https://doi.org/10.3390/diagnostics14030337
ER  -

APA

A, G., S, A., & D, D. (2024). CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics14030337

Source records

pubmed · retrieved 2026-09-24T21:40:39.582Z
europe-pmc · retrieved 2026-09-24T21:40:39.605Z
doaj · retrieved 2026-09-24T21:40:39.597Z